USING ARTIFICIAL ADAPTIVE
AGENTS TO EXPLORE
STRATEGIC LANDSCAPES
BY
STEVEN EDWARD PHELAN
Bachelor of Science (Degree with Honours)
University of Melbourne
Master of Business Administration
Monash University
A Thesis submitted in total fulfilment of the requirements
for the degree of Doctor of Philosophy
School of Business
Faculty of Law & Management
La Trobe University
Bundoora VIC 3083
Australia
November 1997
TABLE OF CONTENTS
ABSTRACT ....................................................................................................................... viii
STATEMENT OF AUTHORSHIP .................................................................................... ix
ACKNOWLEDGMENTS .................................................................................................... x
ii
APPENDICES
(All appendices can be found on the Macintosh format floppy disk located at the back of
the dissertation. The disk also includes runtime versions of all simulations).
1. CODE FOR TOURNAMENT (S4.2)
2. CODE FOR COGNITIVE CAPACITY EXPERIMENTS (S6.1)
a. Initial Model
b. Alternative Model
3. CODE FOR INNOVATION/IMITATION EXPERIMENT (S6.2)
4. CODE FOR PATCHES EXPERIMENTS (S6.3)
a. Patches of Organisations
b. Organisational Patches
3
LIST OF TABLES
TABLE 2.1 MINTZBERG'S FIVE DEFINITIONS OF STRATEGY ................ 11
TABLE 2.2 CLASSES OF EX POST FACTORS LIMITING COMPETITION24
TABLE 2.3 RUMELT'S FIVE FRICTIONS......................................................... 34
TABLE 4.1 PAYOFFS FOR A SIMPLE STRATEGIC GAME ....................... 127
TABLE 4.2 MEAN PERFORMANCE DIFFERENTIALS ............................... 139
TABLE 6.1 ENVIRONMENTAL SCENARIOS................................................. 175
TABLE 6.2 PARAMETER VALUES .................................................................. 177
TABLE 6.3 DESCRIPTIVE STATISTICS FOR PERFORMANCE
DIFFERENTIALS ............................................................................................................ 178
TABLE 6.4 SPEARMAN CORRELATIONAL ANALYSIS ............................. 179
TABLE 6.5 PARAMETER ESTIMATES ........................................................... 179
TABLE 6.6 RESULTS OF CURVE FITTING ................................................... 184
TABLE 6.7 ANALYSIS OF VARIANCE IN RANK PERFORMANCE.......... 197
TABLE 6.8 EXAMPLE OF NK MODEL PAYOFFS, N=3 K=2 ....................... 207
TABLE 6.9 ANOVA RESULTS ........................................................................... 215
iv
LIST OF FIGURES
FIGURE 2.1 DISTINCTION BETWEEN DELIBERATE AND EMERGENT
STRATEGY......................................................................................................................... 12
FIGURE 2.2 PORTER'S FIVE FORCES MODEL ............................................. 17
FIGURE 2.3 CONCEPT OF SCARCITY RENTS ............................................... 21
FIGURE 2.4 ECONOMIES OF SCALE ............................................................... 26
FIGURE 2.5 AMIT AND SCHOEMAKER'S (1993) INTEGRATED THEORY OF
STRATEGY......................................................................................................................... 65
FIGURE 2.6 RELATIONSHIP BETWEEN COMPLEXITY AND RENT
CREATION ......................................................................................................................... 75
FIGURE 2.7 COMPETITION FOR RESOURCES ............................................. 77
FIGURE 2.8 SAMPLE FITNESS LANDSCAPE.................................................. 78
FIGURE 2.9 TWO-DIMENSIONAL FITNESS LANDSCAPE .......................... 80
FIGURE 4.1 CONCEPTUAL MODEL OF A LEARNING CLASSIFIER SYSTEM
............................................................................................................................................ 114
FIGURE 4.2 FLOWCHART OF CLASSIFIER OPERATION ........................ 116
FIGURE 4.3 PRINCIPLE OF ROULETTE WHEEL SELECTION (FROM
FOGEL, 1995) ................................................................................................................... 122
FIGURE 4.4 THE CROSSOVER PROCESS ..................................................... 123
FIGURE 4.5 FREQUENCY OF DOMINANT STRATEGY (N=7) .................. 130
FIGURE 4.6 EXAMPLE OF DUAL CROSSOVER........................................... 135
FIGURE 4.7 PROPORTION OF ARTIFICIAL AGENTS USING DOMINANT
STRATEGY....................................................................................................................... 140
FIGURE 5.1 CONCEPTUAL OVERVIEW OF SIMULATION MODEL ...... 147
v
FIGURE 5.2 SAMPLE FITNESS LANDSCAPE FOR TWENTY INDUSTRIES149
FIGURE 5.3 STRATEGIC LANDSCAPE FOR ONE FIRM ............................ 164
FIGURE 6.1 TWO HYPOTHESISED RELATIONSHIPS BETWEEN
COGNITIVE COMPLEXITY AND PERFORMANCE ............................................... 173
FIGURE 6.2 DISTRIBUTION OF PERFORMANCE (WITH BOX PLOT) .. 178
FIGURE 6.3 RESULTS OF SECOND COGNITIVE CAPACITY EXPERIMENT .. 183
FIGURE 6.4 EXAMPLE OF ROULETTE WHEEL SELECTION IN
OPERATION .................................................................................................................... 186
FIGURE 6.5 EXAMPLE OF NOISY BID REGIME IN OPERATION ........... 187
FIGURE 6.6 DISTRIBUTION OF TOTAL CUMULATIVE PERFORMANCE197
FIGURE 6.7 INTERACTION OF STRATEGY AND IMITABILITY ............ 198
FIGURE 6.8 INTERACTION OF STRATEGY, IMITABILITY AND
AMBIGUITY .................................................................................................................... 199
FIGURE 6.9 MEAN PROFIT BY PATCH SIZE AND COMPLEXITY ......... 214
FIGURE 6.10 PROFIT DIFFERENCES BY DECISION STYLE AND
COMPLEXITY ................................................................................................................. 219
FIGURE 6.11 DIFFERENCES IN ACTIVITY LEVEL BY COMPLEXITY AND
DECISION STYLE ........................................................................................................... 220
6
ABSTRACT
Recent developments in computer science have made it possible to
simulate whole populations of artificial agents within the confines of a single
personal computer. These artificial agents can be programmed to act in ways
that mimic the behaviour of physical, biological and economic agents. Such
developments come at a time when researchers in strategic management have
been complaining about a lack of data to test theoretical predictions. There is a
perception that the ability to generate novel hypotheses has greatly exceeded
the ability to empirically test them. This dissertation investigates whether
agent-based simulation can be used to test hypotheses in strategic management.
SELESTE is a highly-abstract artificial world that was developed using
concepts from Amit and Schoemaker‟s (1993) integrated theory of strategy.
SELESTE provided the environment, or strategic landscape, for our artificial
agents to explore. The agents themselves were modelled using an algorithm
from artificial intelligence known as a learning classifier system. Artificial
agents in SELESTE were shown to behave in similar ways to human agents.
Three studies were selected to showcase the range of problems that
agent-based simulation can address. The first problem investigated whether
differences in the cognitive capacity of firms led to sustainable differences in
performance. The importance of differences in cognitive capacity was shown to
decline as the absolute level of cognitive capacity increased. The second study
investigated the conditions under which imitation proved a superior strategy to
innovation. It was revealed that imitation was a powerful strategy under all but
the most extreme conditions. The final study revealed that firms that divided
their operations into multiple profit-centres or „patches‟ performed better than
firms organised as a single profit-centre.
It was concluded that agent-based simulation represented a useful method
for exploring problems in strategic management. Calls were made to establish a
vii
research program in agent-based simulation to build on these findings and to
refine existing techniques.
viii
STATEMENT OF AUTHORSHIP
Except where reference is made in the text of the thesis, this
work contains no material published elsewhere or extracted
from a thesis presented by me for another degree or diploma.
No other person‟s work has been used without due
acknowledgment in the main text of the thesis.
This thesis has not been submitted for the award of any other
degree or diploma in any other tertiary institution.
Steven E. Phelan
November 20, 1997
ix
ACKNOWLEDGMENTS
I would like to thank my wife, Carmel, and my children, Kathleen and
Michael, for the many sacrifices they have made during the completion of this
dissertation. Thank you for the faith, hope and love that is my inspiration.
Thanks also to my supervisor, Professor Greg O‟Brien, for accepting me
as a PhD candidate from AGSM and giving me the space to do it my way.
Also, thanks to Marcus Wigan who freely gave numerous hours of his time to
support this project.
Finally, I would also like to thank Professors Aiken and Maddock who
ensured that I had the resources to get on with the job.
x
INTRODUCTION
1
Imagine , if you will, that you are given a box about one metre square which
contains a miniature economy. On the outside of the box is a set of dials and
levers that can be used to adjust the policy settings in this petite economy. The
top of the box is covered by a thick sheet of glass which has the effect of
magnifying the inhabitants of the box. By peering through different sections of
the glass, you are able to watch the various inhabitants of this economy go about
their daily life. Workers can be seen selling their labour to entrepreneurs in return
for wages. Entrepreneurs, in turn, sell the surplus of this labour in tiny bustling
marketplaces, and deposit their profits in Lilliputian banks. All manner of goods
and services are produced in the economy, and you are amazed at the ability of
the inhabitants to organise their affairs; running down excess inventory or
increasing production as required; matching demand with supply. In an hour of
your time, a century passes within the box. As you watch, companies are
founded, expand, and die. Like any economy, there are periods of boom and bust
with occasional depressions and speculative bubbles. By playing with the knobs
and levers on the outside of the box you are able to smooth out economic cycles,
introduce technological improvements, and even alter the marginal rate of
substitution of labour for capital. Some of these changes produce happiness for
the inhabitants, generating high levels of employment and disposable income.
Other changes bring misery and great suffering, with families cast from their
homes and production levels brought to a standstill. When you grow tired of a
particular set of circumstances, or are occasionally moved by the level of
suffering you have inflicted, you reach for the red „reset‟ button located at the
rear of the box and everything is automagically restored to its starting position.
The preceding passage skilfully combines science fact with science
fiction to create the illusion that it is possible to „enslave‟ or „imprison‟
economic actors and study their behaviour for our own amusement and
edification. The passage is illusory because technology has not reached the
stage where we can build artificial microworlds such as this. Science, however,
has a habit of turning fiction into fact.
Forty years ago, the nascent field of artificial intelligence promised to
build a computer that could think as well as a human. While researchers are not
as optimistic as they once were about the prospects of creating a sentient
computer, they have learned quite a bit about modelling human intelligence.
Algorithms now exist that enable us to create a piece of software, called an
artificial adaptive agent, that can learn about its simulated environment in much
the same way that humans learn about their own physical environment. Indeed,
1
such is the power of modern computing that we can actually create a whole
population of these artificial adaptive agents in the memory of one personal
computer.
The biological sciences were quick to grasp the implications of this new
technology. The burgeoning field of artificial life involves populating
simulated ecosystems, or biological landscapes, with artificial animals (known
as animats) that enable researchers to observe how such ecosystems evolve
over time (Levy, 1992). Real world biological phenomena such as survival of
the fittest, symbiosis and ecological niches have been observed to occur in
artificial life populations. Similarly, social scientists have also started to
investigate the properties of artificial adaptive agents in simulated social worlds
and to consider the opportunities they offer to expand our knowledge of social
processes (Bainbridge et al., 1994; Conte, Hegselmann, & Terna, 1997; Gilbert
& Doran, 1994).
In strategic management, Erhard Bruderer (1993) was able to
demonstrate that artificial adaptive agents were able to learn strategic problems
in much the same way as human subjects. Bruderer‟s research suggests that we
should be able to populate “strategic landscapes” with artificial adaptive agents.
Research in biology and elsewhere suggests that we might be able to learn
about the real-world from this endeavour. This dissertation takes up the
challenge by asking whether the study of artificial adaptive agents on a strategic
landscape can provide novel insights into strategic management. In the next
section, we endeavour to argue that this approach is more than simply „a
solution in a search of a problem‟. At this point in its development, strategic
management genuinely has a need to embrace new methodologies to address
old questions.
1
with apologies to Stanislaw Lem (1981)
2
Rationale
At the core of strategy content research, a subfield of strategic
management, lie two fundamental questions: „What makes some firms more
successful than others?‟ and „How do I make this particular firm successful?‟
(Bowman, 1995). Early researchers, such as Chandler, Mintzberg, Bower and
others, relied on case studies, company histories and personal experience to
generate a wealth of theories and insights into corporate practice (Bower, 1986;
Chandler, 1962; Mintzberg, Raisinghani, & Theoret, 1976). However, this
period has also been characterised as a time of vague generalities, wild
speculations and second-rate inferences drawn from small unrepresentative
samples.
As the field has matured, there has been a move away from small scale
exploratory studies towards more rigorous empirical testing using large scale
datasets (Schwenk & Dalton, 1991). This shift has corresponded with greater
calls for methodological rigour and theoretical precision (Camerer, 1985;
Montgomery, Wernerfelt, & Balakrishnan, 1989). In the words of Schendel and
Hofer (1979):
...the field now has sufficient variety of concepts and hypotheses of both a
descriptive and normative character to form the basis of a scholarly discipline. At
the same time, most of these concepts and hypotheses have never been subjected
to adequate empirical testing to determine their domain of applicability or, in
some cases, whether they are even valid for any set of organizations (Hofer and
Schendel, 1979, quoted in Lampel and Shapira, 1995).
Advocates of this view have promoted rigorous empirical testing based on
carefully defined constructs to dispel the vague generalities and fuzzy
inferences of the past. The result has been a clear trend towards the use of
quantitative methods in published research and a decline in studies employing
qualitative methods (Lampel & Shapira, 1995).
According to Lampel and Shapira (1995), not all forms of inquiry are
equally amenable to quantification.
3
“The increase in data collection costs does not affect all research programs
equally. Research programs whose constructs can be measured reliably using
publicly disseminated information such as Compustat, CRSP, Value Line
services, or FTC data face less resource constraints than research programs
whose constructs require private or difficult to obtain data” (Lampel & Shapira,
1995, p. 135, italics in original)
Paradoxically, the push for more rigour in strategic management has seen a
decline in relevance as researchers are drawn into using data that is reliable,
easy to obtain (both in terms of time and money) and easily quantifiable.
Research has become data-driven rather than theory-driven; the availability of
data determining the questions that can be asked and answered. Critics are not
without justification when they describe quantitative studies as sterile and
simplistic with an inability to capture change, complexity and uniqueness
(McKelvey, 1997). Even researchers who work with these large-scale public
databases recognise that the scarcity and inadequacy of available data
represents a serious problem (McGahan & Porter, 1997).
The use of agent-based simulation addresses many of the key concerns of
both parties in this debate. On the one hand, simulation data is rigorous. Every
variable in the simulated system can be measured with a high degree of
reliability and accuracy. Moreover, the rules that govern the interaction of
agents within the system are totally transparent and open to public scrutiny and
debate. Simulations are also capable of producing large amounts of data to
drive even the greediest statistical model. Unlike researchers in the real world,
simulation researchers are not limited to one version of events. The use of
multiple simulation runs allows many different scenarios to be investigated
(Brehmer & Dorner, 1993).
Critics of quantitative methods have criticised their ability to capture
change, complexity and uniqueness. By allowing a computer to trace the
evolution of a model over time, almost all simulation models are dynamic by
design. Simulations are therefore ideal environments in which to study change,
flux and evolution. Simulations also allow the researcher to construct models
of almost arbitrary complexity (but with the proviso that complex models may
4
be almost as difficult to interpret as the real world). Finally, agent-based
simulations provide a breakthrough in modelling uniqueness. In agent-based
systems, aggregate data emerges from the interplay of individual agents. Each
agent acts idiosyncratically to generate its own life history and actions are
constrained by past events (ie. they are path-dependent).
The scarcity and inadequacy of data sources in strategic management has
been identified as an important problem confronting researchers in the
discipline. It is the unexplored potential of agent-based simulations to alleviate
data scarcity, by combining the rigour of quantitative approaches with the
complexity and uniqueness of qualitative approaches, that provides the
rationale for this study. In turn, creating a new data source allows us to test
theories that have previously been considered untestable due to the cost or
difficulty of acquiring reliable volumes of data.
Objectives and Definitions
This dissertation is primarily concerned with exploring the effectiveness
of agent-based simulation as a methodology for strategic management. Its three
objectives are therefore methodological in nature:
1.
to design a theoretically-inspired strategic landscape
2.
to design of set of artificial adaptive agents to explore this landscape
3.
to use the system to generate novel insights into strategy
The terms „artificial adaptive agent‟ and „strategic landscape‟ are defined in
more detail below.
What is an artificial adaptive agent?
In the emerging science of complexity, a complex system is defined as
any network of interacting agents (or processes or elements) that exhibits a
dynamic aggregate behaviour as a result of the individual activities of its
5
agents. An agent in such a system is adaptive if its actions can be given a value
(performance, utility, payoff, fitness etc.) and the agent behaves so as to
increase this value over time. Thus, a complex adaptive system is “...a complex
system containing adaptive agents networked so that the environment of each
agent includes other agents in the system” (Holland & Miller, 1991, p. 365).
A firm is an example of an adaptive agent acting in a complex adaptive
system. Firms act to increase their profits (or shareholder value) over time and
are, in this sense, adaptive. In addition, a firm‟s profitability is at least partially
dependent upon the actions of its competitors. This interactivity among agents
is a feature of any complex system.
When computer simulations are used to model complex adaptive
systems, the actors in the model are referred to as „artificial adaptive agents‟
(Holland & Miller, 1991). Artificial adaptive agents are usually programmed
using artificial intelligence algorithms that allow them to act in rational, or
human, ways in their environment.
What is a strategic landscape?
In game theory, a payoff is the reward (or punishment) received by a
player for selecting a given strategy or action (Dixit & Nalebuff, 1991). With
two or more players, the payoff can depend on the joint actions of each player,
and it is possible to create a payoff matrix which details the payoff to a player
given the various actions of every other player.
A fitness landscape is a graphical representation of a payoff matrix that
uses the height of the graph to represent the level of payoff or „fitness‟ (see
Figures 2.8 and 2.9). Fitness landscapes have higher and lower areas of fitness
that are referred to as peaks and valleys. Unlike a payoff matrix, the shape of
these peaks and valleys changes over time as the actions of the agents in the
system deform the fitness landscape. Agents are therefore likely to face a new
fitness landscape in each time period. A „rugged‟ fitness landscape is one with
6
many peaks and valleys while a „smooth‟ fitness landscape has few peaks and
valleys.
A strategic landscape is a fitness landscape where the height of the
landscape represents the level of profit available to a firm given the joint
strategies of all the other firms in the model. The height of the landscape will
depend not only on the actions of competitors but also on the level of wealth, or
munificence, in the general environment. Metaphorically, firms can be said to
„climb‟ or „explore‟ a strategic landscape in search of higher profits. The best
adaptive agents climb to the top of the highest peaks.
Thus, the first objective of this study is to build a strategic landscape
inspired by theoretical distinctions in the strategic management literature. The
second objective is to design an artificial adaptive agent that is effective at
climbing the peaks of the strategic landscape we have designed. The final
objective is to use this model to contribute to our knowledge in strategic
management.
Structure of the Thesis
The next chapter, Chapter 2, opens with a discussion of Mintzberg‟s five
well-known definitions of strategy and provides a justification for the particular
perspective on strategy adopted throughout this study. This is followed by an
extensive review of several major theories of strategy content (the sub-field
area of strategic management research concerned with analysing the sources of
competitive advantage). This discussion provides the background for the
development of four central axioms that are used later in the dissertation to
develop the strategic landscape.
Computer simulation (and agent-based simulation in particular) is a
relatively new methodology in strategic management and its use needs some
justification and elaboration. The material in Chapter 3 performs this function;
serving as a small review on the use of simulation as a methodology. It begins
7
by providing a comprehensive definition of simulation, followed by a
discussion of some of the more traditional justifications for using simulation
methodology. The next section provides a taxonomy of simulation methods;
distinguishing between simulations on the basis of their purpose and
fundamental approach to modelling the phenomena of interest. The final
section in Chapter 3 discusses the limitations of simulation.
Chapters 4 and 5 are primarily concerned with the development of
SELESTE, our agent-based simulation model. The first half of Chapter 4
provides a detailed discussion of the „learning classifier system‟ algorithm used
to direct and control many of the artificial adaptive agents in SELESTE. The
second half of the chapter compares the operation of a learning classifier system
with both human subjects and other algorithms that have been used in the field.
Chapter 5 makes use of the theoretical axioms developed in Chapter 2 to
describe the objects in the SELESTE environment and the way in which these
objects interact with one another to form the strategic landscape. This is
followed by a discussion of the agent-landscape interface and concludes with an
overview of the operation of the entire system.
A cross-section of studies centred around the SELESTE model are
presented in the penultimate chapter, Chapter 6. Each study is a substantive
investigation of an interesting strategic question, which taken together, serve to
highlight the flexibility and capabilities of the SELESTE model. The first study
chooses to investigate whether differences in the cognitive capacity of firms
leads to sustainable differences in performance. This is followed by a study that
explores the conditions under which imitators can outperform innovators, while
the final study compares the relative merits of integrated versus divisionalised
strategic control systems.
The final chapter, Chapter 7, opens with a discussion of the contributions
of the dissertation to knowledge in strategic management. This is followed by
an acknowledgment of the limitations of the study, future research directions,
and the conclusion.
8
A Review & Synthesis of the Theory of
Strategy
"Where judgement begins, there art begins...Strategy theory, therefore, will
content itself to assist the commander to the insight into things which, blended
with his whole thought, makes his course easier and surer, but never forces him
into opposition with himself in order to obey an objective truth. All principles,
rules and methods exist to offer themselves for use as required, and it must
always be left for judgement to decide whether or not they are suitable."
-
Karl
von
Clausewitz
This chapter has a number of objectives. It begins by defining the concept
of strategy; a definition that draws heavily on the work of Mintzberg. In the
second part, we review the contributions and shortcomings of three dominant
theoretical perspectives on normative strategy-making, namely the competitive
forces, resource-based and evolutionary dynamics schools. This is followed by
an examination of the rationale and means by which researchers have attempted
to integrate these three explanations into a comprehensive theory of firm
success. The chapter concludes with a discussion of the theoretical principles
adopted in the current study. Our theoretical stance is substantially influenced
by the earlier discussions on an integrated theory of strategy but also introduces
elements of evolutionary economics and bounded rationality to construct a
metaphor of strategy as search on a fitness landscape.
What is Strategy?
In common parlance, the term strategy can be defined as a consciously
intended course of action to achieve some goal or objective. A strategy is made
in advance of the actions to which it applies and is often stated explicitly in a
formal document known as a plan (Mintzberg, 1987). Mintzberg, a taxonomist
par excellence, has also suggested four other contexts in which the term
strategy may be applied (see Table 2.1).
9
Table 2.1 Mintzberg's Five Definitions of Strategy
Strategy as...
Plan
Ploy
Position
Pattern
Perspective
Description
[a] consciously intended course of action...made in advance
of the actions to which they apply...often...stated explictly in
formal documents known as plans.
a specific manuever intended to outwit an opponent or
competitor
strategy is...any viable position, whether or not directly
competitive, ie.occupying a niche in the environment
a pattern in a stream of actions...consistency in behavior,
whether or not intended
commitments to a way of acting and responding
To the extent that a strategy is an intended set of actions, the Strategy as
Ploy and Strategy as Position definitions can be seen as complementary to the
definition of Strategy as Plan, rather than as alternative definitions. Both
definitions imply an action or set of actions to achieve an objective (whether
that objective is to outwit an opponent or occupy a niche position). Thus, a
strategy or plan may consist of a series of ploys, or possibly an intention to
attain a given position in the market.
The definition of Strategy as Pattern is more problematic. Mintzberg
creates a distinction between deliberate strategy and emergent strategy (see
Figure 2.1). Mintzberg argues that a given pattern of actions or results (which
1
he labels realized strategy) may be the product of a deliberate set of actions, or
the result of actions taken without reference to an intended plan. The latter
actions are typically undertaken in response to events unforeseen at the time of
the development of the plan. Mintzberg is quick to label these reactive actions
as "emergent strategy" which he claims creates a capacity for strategic learning.
1
An intended strategy that is successfully implemented is called a deliberate strategy,
while an intended strategy that fails to be implemented is known as an unrealized strategy.
10
Figure 2.1 Distinction between deliberate and emergent strategy
This definition allows an external observer to label a consistent pattern of
actions by a particular organisation as "strategic" irrespective of whether those
in the organisation acknowledge the existence of an intended strategy or plan.
Given the difficulties inherent in studying strategy (see Chapter One), the
development of this behavioural definition is not surprising. Researchers are
often forced to infer strategy from action.
However, there is a subtle distinction between hypothesising that a set of
actions may have been the result of a deliberate strategy, and stating that any
consistent set of actions forms a strategy (Kenyon & Mathur, 1993). We
believe the latter definition of strategy to be an inappropriate use of the word as
it is understood in common parlance. Of course, this is not saying that success
cannot occur in the absence of (or even despite) a deliberate strategy. There are
many examples of firms being successful without any deliberate strategy. We
are simply opposed to the need to label any successful set of actions as
"strategic".
Nor are we saying that having a long-term plan implies that an
organisation must follow a fixed set of routines or actions that cannot be
varied. A plan must be constructed on the basis of conjectures about the future
state of the world. To a greater or lesser extent, those conjectures will be
incorrect (Mintzberg, 1994; Stacey, 1993). A plan can also not hope to specify
all the details required to perform a given set of actions. Thus, local managers
must be given the freedom to adapt their actions to the situation at hand. It is
our contention that a key characteristic of a deliberate strategy is that it
11
provides active guidance for this adaptive search process (ie. that the reaction
to unforeseen events does not have to be unplanned).
The final definition, Strategy as Perspective is, in our opinion, not the
same as the strategy itself. In order for a strategy to be executed it must be
disseminated in some fashion throughout the organisation in order for
appropriate actions to be taken in implementing the strategy. The members of
an organisation may share the same view or set of beliefs about a strategy but
this is not the same as saying that the strategy is then strictly equivalent to the
shared perspective of the group. The perspective arises because of the strategy,
the strategy does not arise from the perspective.
Thus, the current study strongly takes the view that a strategy is an
intended, but contingent, set of actions taken to achieve a particular goal or
objective. This recognises the fact that a strategy must also consist of a set of
heuristics2 which ensure that managers act in a consistent and co-ordinated
manner to changing circumstances, regardless of whether those changes were
perceived in the initial plan or not. The German military strategist, Karl von
Clausewitz, was adamant that strategy theory merely offered useful heuristics,
not certainties:
"All principles, rules and methods exist to offer themselves for use as required,
and it must always be left for judgement to decide whether or not they are
suitable. Theory must never be used as norms for a standard, but merely as an aid
to judgement" (quoted in DeWit & Meyer, 1994).
The positive role for strategy outlined above is certainly not fashionable
in today's popular business literature. Strategic planning has waned in
popularity in corporate circles in recent years (Mintzberg, 1994). Many
managers perceive that an increasingly turbulent environment makes detailed
forward planning impossible. Management gurus are urging managers to
abandon strategy and focus on honing their organisational learning capabilities:
2
Herbert Simon first used the term heuristic to describe the strategies of experienced
chess players. An heuristic is a rule of thumb that can be used to attain success but is not
guaranteed to do so. An heuristic is not a recipe for winning, but using it increases the chance
of success.
12
to become better at reacting to changes rather than trying to predict changes
(Stacey, 1993).
It is ironic that the organisational learning paradigm can be viewed as an
heuristic for guiding managerial action in turbulent environments. It therefore
could be viewed as a strategy (but not a plan) under our definition. One of the
major goals of this study is to identify viable heuristics and attempt to specify
the conditions under which each heuristic might increase the chance of firm
success.
Towards a Theory of Strategy
The year, 1980, was a watershed in the development of strategic
management. That year saw the founding of the Strategic Management Journal
and the publication of Michael Porter‟s top-selling “Competitive Strategy”
(Porter, 1980). It also represented a change from an emphasis on the procedural
aspects of strategic planning to an emphasis on creating and sustaining
economic rents.
“The single most significant impact of economics in strategic management has
been to radically alter explanations of success. Where the traditional frameworks
had success follow leadership, clarity of purpose, and a general notion of 'fit'
between the enterprise and its environment, the new framework focused on
impediments to the elimination of abnormal returns.” (Rumelt, Schendel, &
Teece, 1991, p. 5)
The concept of abnormal returns (or economic rent) was derived from
introductory economics. In the economist‟s model of perfect competition, firms
set a price for their production that covers the cost of inputs such as labour and
raw materials. Input costs also include a return to capital, which represents the
residual after all other costs, or profit. Capital was considered extremely
mobile. If firms failed to produce a level of profit that adequately compensated
the owners of capital then they withdrew their support and the firm failed. If the
firm produced above-average profits (or economic rents) then entrepreneurial
capitalists would inject funds into new ventures, thus expanding output in the
industry, and driving down prices and profits to an equilibrium point just high
13
enough to compensate investors. The invisible hand of competition would thus
ensure that risk-adjusted average returns were the same across all firms and
industries (Oster, 1982).
There is considerable evidence from economic historians to suggest that
this atomistic model of competition was obsolete by the late 19th century
(Chandler, 1990; Lazonick, 1991). Particularly in the United States, several
capital intensive industries, such as communications, railroads and steel,
became dominated by one or two major players. These major players - AT&T,
Western Union, Rockefeller, JP Morgan - earned returns far in excess of the
average. For example, AT&T in the first sixteen years of its life recorded an
average return on equity of 46% when industrial returns were averaging 8%
(Brock, 1981). This period also saw the rise of a managerial class. These huge
conglomerates were too large to be run by one entrepreneur and decision
making had to be delegated to a management bureaucracy. It is no coincidence
that the first business school in the US, the Wharton School at the University of
Pennsylvania, was established around this time (1881). Ironically, economists
and strategists are still arguing whether sustained economic rents exist.
However, recent large scale empirical studies have concurred on the existence
of large variations in profitability across firms (Roquebert, Phillips, & Westfall,
1996; Rumelt, 1991; Schmalansee, 1985) and the persistence of abnormal
returns (Jacobson, 1988).
Since 1980, the normative research agenda in strategic management has
been concerned with answering a single question: Why do some firms perform
better than others? Drawing on the work of Teece, Pisano and Shuen (1994), it
is possible to classify responses to this question into three distinct paradigms:
competitive forces, resource-based, and evolutionary dynamics. A passing
familiarity with these paradigms is necessary to understand subsequent
attempts at theoretical integration.
14
The Competitive Forces Paradigm
The success of giant corporations at the end of the 19th Century led to an
outcry in the US against various abuses of market power, including price fixing
and hostile takeovers. The Sherman Act of 1890 prohibited price fixing and
other anti-competitive measures. A new branch of economics, industrial
organization economics, evolved to advise the government on the nature and
need for regulation of industry. Following the work of Mason and Bain (Porter,
1981), research in industrial organization was dominated by the structureconduct-performance
paradigm.
Within
this
paradigm,
performance
differentials between industries could be explained by the structural
characteristics of each industry. These structural characteristics led firms to
adopt different strategies (conduct) which resulted in performance differences
between industries.
In 1980, Michael Porter distilled three decades of research on industrial
organization into a set of prescriptions for managers. Whereas industrial
organization theory had seen the firm as the servant of industry structure,
Porter‟s genius was to reverse the industrial organization goal of promoting
perfect competition and instead focus managers on raising industry profitability
by consciously modifying industry structure to their own advantage. Even when
managers could not change industry structures they were still encouraged to use
their discretion to enter profitable industries and exit less profitable industries.
Thus, industry-based strategy consisted of two discrete sets of decisions:
entry/exit decisions and market power decisions (where market power is
defined as the ability to alter industry structure).
15
Potential Entrants
Power of
Suppliers
Rivalry
Power of
Buyers
Substitutes
Figure 2.2 Porter's Five Forces Model
Porter characterised industry profitability as the interaction of five forces
(see Figure 2.2). The stronger these forces the less profitable the industry.
Porter's industry model has had little empirical testing in the strategic
management literature but numerous studies in industrial organization
economics have attested to sustained profitability differentials between
industries (Grant, 1991; Oster, 1990; Ravenscraft & Wagner, 1991;
Schmalansee, 1985). Rather than oppose the extant data, criticisms of Porter's
model have focused on alternative interpretations of the facts.
As we have already seen, the model of pure competition advanced by
economists holds that economic profits should be zero across the economy.
This implies that risk-adjusted rates of return should be constant across
industries and firms (Oster, 1990). However, the research in industrial
organization economics has clearly demonstrated that profitability varies
systematically across industries. Porter explained these industry differences by
alluding to differences in the five forces (or strategic industry factors) acting on
industries. Of course, if industry forces were the only determinants of
profitability among firms then we would expect to see the same rates of return
for all firms within the same industry. The fact that firms within the same
industry did not earn the same rate of return had been self-evident long before
Porter wrote his first book3. The response to these facts was to construct a
3
This point was not demonstrated statistically until the work of Rumelt (1991).
16
theory of strategic groups (Caves & Porter, 1977; Fiegenbaum, Sudharshan, &
Thomas, 1990; Hatten & Hatten, 1987; McGee & Thomas, 1986; Oster, 1982).
It was postulated that another set of forces (termed mobility barriers) prevented
groups of firms within industries from imitating the strategies of the most
successful group.
The theory of strategic groups had intuitive appeal because it fitted the
observation that many industries had a small number of large firms in a
dominant position and a larger number of followers with lesser market share
and profitability. Successful niche or regional players could also be easily
defined as additional strategic groups.
Inevitably, even firms that have been classified within the same strategic
group were observed to exhibit significant variations in performance and
profitability (Cool & Schendel, 1987). Returns within industries have been
found to vary three to five times as much as returns between industries
(Rumelt, 1984; Rumelt, 1991). Rumelt has wryly commented that:
"...there is no theoretical reason to limit mobility barriers to groups of
firms...I...use the term isolating mechanism to refer to phenomena that limit the
ex post equilibriation of rents among individual firms" (Rumelt, 1984, p. 567).
One is reminded of the story of the old lady who insisted that the Earth
was borne on the back of a tortoise. "Ah!", said the village philosopher, "but
what does the tortoise stand on?". "Young man", came the reply from the old
woman, "everyone knows that its tortoises all the way down". In a similar
manner, the industry-based theorists must invent layer upon layer of "forces" to
create the barriers necessary to explain the observed differences in profitability
among firms. The next section will explore the resource-based view of strategy
which provides a more parsimonious explanation for performance differentials
amongst firms in the same industry.
17
The Resource-based View of Strategy
The resource-based view of strategy has postulated that differences in
firm performance arise because successful firms possess valuable resources and
capabilities not possessed by other firms, thus allowing firms with these
valuable assets to earn a form of scarcity rent. Resources are defined as inputs
into the firm's operations rather than products or services (Wernerfelt, 1984).
Examples of resources include patents, capital equipment and skilled human
resources. A „capability‟ is defined as the capacity to perform a task or activity
that involves complex patterns of coordination and cooperation between people
and other resources (Grant, 1991; Schulze, 1994). Capabilities include research
& development, excellent customer service, and high quality manufacturing.
Strategic assets (resources or capabilities) must ultimately be embedded
in end products that create value for customers (Prahalad & Hamel, 1990;
Wernerfelt, 1984). This value can be created in a number of ways, including
superior quality, service, innovation or cost. Greater product differentiation
results in increased margins either by premium pricing, cost minimisation or
both. Fungibility, or the ability to apply strategic assets to a wide range of
products and applications, is also a desirable trait (Penrose, 1959; Prahalad &
Hamel, 1990).
Scarcity Rents
Scarcity rents arise whenever demand exceeds available supply and
supply cannot be expanded (see Figure 2.2). Ricardian rents occur when the
supply of a resource or product is permanently fixed (such as arable land),
while quasi-rents occur when the supply is only temporarily fixed. Scarcity
rents are often difficult to distinguish from monopoly rents; supply being
artificially constrained to produce monopoly rents (Winter, 1995). It is often
very difficult to tell whether a corporation is deliberately constraining supply or
not. Where the competitive forces paradigm sees barriers to entry creating
18
monopoly rents, resource-based strategists interpret supply constraints as
scarcity rents created by rare cases of infra-marginal efficiency.
Obviously if a strategic asset is unique it has a greater chance of creating
scarcity rents, but strategic assets do not necessarily need to be unique. A
relatively rare resource or capability can be shared among a limited number of
competing firms and still generate scarcity rents so long as the total output, Qs,
is less than the equlibrium output, Qe (see Figure 2.3).
Figure 2.3 Concept of Scarcity Rents
Durability
Given that most above-average profits do not persist indefinitely, firms
must be generating one or more forms of temporary or quasi-rents (Jacobson,
1988). The durability and imitability of a strategic asset have been identified as
important attributes in determining the sustainability of a rent-producing period
(Peteraf, 1993). A strategic asset is durable to the extent that the benefits it
conveys do not decay over time (ie. the asset is self-sustaining). Imitability
refers to the ability of competitors to copy or duplicate the strategic asset.
19
On the face of it, determining asset durability does not seem to be a major
problem. Accountants are quite skilled in maintaining fixed asset registers and
charging a schedule of depreciation to cover the wear-and-tear on these assets.
Unfortunately, the most important strategic assets tend to be invisible on the
balance sheet with the majority of successful companies having large market to
book ratios (Itami, 1987). This hidden value represents factors such as brand
equity, human capital, R&D capability and goodwill.
Identifying and nourishing these invisible strategic assets can be difficult,
and managers can unwittingly destroy strategic assets. One of the major
criticisms of the downsizing movement of the early 1990s was that large scale
redundancies tended to “throw the baby out with the bathwater”. Untargeted
redundancies often resulted in the departure of the most highly skilled and
experienced people who had options for reemployment. Moreover, capricious
and seemingly random downsizing demoralised survivors and shattered loyalty
and commitment to the organisation. But what had been lost? Accounting
reports merely showed a reduction in labour expense. Nevetheless, value
attributable to employee goodwill and human capital (that did not appear on the
balance sheet) had also been lost.
Some classes of strategic assets are not owned by the firm. Labour is the
most obvious example and it displays a high degree of factor mobility.
Employees literally rent their skills and are able to withdraw from the firm or
transfer their skills to a competitor at any time (subject to legal sanctions).
Durable strategic assets must therefore demonstrate a degree of factor immobility.
Pareto or quasi-appropriable rents have been identified as an
important elements in the creation of factor immobility (Peteraf, 1993).
The concept of Pareto rents is best illustrated with an example. Holders
of 7-11 franchises must take significant business risks. They lease a store,
maintain minimum stock levels, staff the outlet, and pay franchise fees back to
the 7-11 franchisor. Having a large network of outlets creates a strategic asset
for the 7-11 organisation, but one which the franchisor does not own or control.
20
Recently, a local 7-11 franchisee decided to defect from the network. He
renamed his store Star-11, found alternative suppliers, and traded from his
existing customer base. Why don‟t more stores defect? The reason is that
membership of the 7-11 franchise carries significant benefits for the franchisees
including brand recognition, network advertising and group buying power. The
7-11 organisation has co-specialised assets that add value as a package. By
opting out of the network, the Star-11 owner lost access to those benefits.
Pareto rents occur when the value of an asset in one application exceeds the
value of the asset in its next best use. So, for example, a store in the 7-11
network is more valuable than a stand-alone store. The 7-11 franchisee must be
guaranteed greater earnings than a stand-alone convenience store to be enticed
into entering the franchise agreement. The 7-11 organisation profits from the
difference between the value of the network and the payments to franchisees.
Thus, the Pareto rent is shared and subject to negotiation.
The 7-11 example can easily be extended to skilled labour. Skilled
researchers working in a laboratory with a talented team, superior equipment,
and access to funding add more value than a lower quality lab. This added
value represents a Pareto rent and the division of the rent between the worker
and owner is once again subject to negotiation.
Imitability
The possession of Pareto rents is not a sufficient condition for a sustained
competitive advantage (Peteraf, 1993). The requirement that strategic assets are
difficult to obtain or control is central to the entire concept of rent creation4.
Competitors have a wide range of methods for acquiring scarce resources. Most
often, competitors will attempt to replicate or copy a strategic asset within their
own firm. If this proves difficult they may resort to buying the asset on the
open market, acquiring the holding company through takeover or merger, or
even engaging in industrial espionage and stealing the strategic asset.
4
whether monopoly or scarcity rents
21
Intellectual property, such as computer software, may be obtained by
appropriation of key personnel. The substitution of one strategic asset for
another may also be an effective way of neutralising a competitive advantage
(Dierickx & Cool, 1989).
Some strategic assets are more impervious to imitation than others. For
example, invisible assets, such as brand name or corporate culture, are
generally recognised as more difficult to replicate or acquire than fixed assets
such as specialised machinery (Itami, 1987). Determining the factors that make
resources difficult to imitate has been a key area of research in the resourcebased literature. The majority of the debate on imitability has concerned ex post
limits to competition which are the factors that prevent competitors from
imitating a strategic asset after it has been introduced into a market (Peteraf,
1993). Although a large number of factors have been identified in the literature,
they can generally be broken into four major classes (see Table 2.2).
Table 2.2 Classes of Ex Post Factors Limiting Competition
Class
Legal Sanction
Size
Information
Time
Factors
• patents & trademarks
• land titles
• legally enforceable contracts
• government legislation & statute
• inframarginal efficiency
• capital requirements
• network economies
• causal ambiguity
• social complexity
• tacit knowledge
• ignorance
• search costs
• path dependency
• positive feedback
• time compression economies
• inertia
Legal Sanctions
The possession of legal control or property rights over certain types of
strategic assets is certainly an important factor in preserving firm heterogeneity.
However, the status of legal sanction as a creator of scarcity rents is equivocal.
22
Legal sanctions are usually viewed as artificial constraints on business and as
such create monopoly rents. For instance, McDonald‟s golden arches symbol is
protected from imitation by a registered trademark. Likewise, the ownership of
scarce physical resources, like a favourable retail location or mining claim, is
protected by land titles that prevent competitors from illegally acquiring or
using the property. Similarly, contract law (where enforceable) allows firms
control over scarce or valuable resources such as specialised labour. Ultimately,
the fact that government legislation or statute can create (and destroy) rents for
any firm or industry cannot be disputed. However, legal sanction should not be
classified as creating scarcity rents. They have been included here because legal
sanctions might act to preserve an advantage obtained by acquiring an initially
scarce resource.
Size
The genesis of the industrial organisation movement originated in
government attempts to regulate large corporations at the end of the last
century. Industrial organisation has traditionally equated size with the ability to
create barriers to entry and establish monopoly rents. Resource-based theorists
have argued that some advantages naturally accrue to large firms in addition to
any deliberate attempts to deter entry and create monopoly rents. The term
“asset mass efficiencies” has been used to refer to situations where “adding
increments to an existing asset stock is facilitated by possessing high levels of
that stock...the underlying notion is that success breeds success” (Dierickx and
Cool, 1989, p. 1507).
The classic scale economy relationship is depicted in Figure 2.4. Unit
costs fall from Ca to Cb as output increases from Qa to Qb. Let us construct a
two producer market with firms A and B producing outputs of Qa and Qb
respectively. If the demand at price, P=Ca, equals Qa+Qb then the market will
clear at an average unit cost of Ca, and producer B will earn scarcity rents
(attributable to inframarginal efficiency) equal to Qb.P.(Ca-Cb). Rents will
23
continue to be earned so long as demand is less than 2.Qb. Similar arguments
can be presented for economies of scope and experience curve effects.
Figure 2.4 Economies of Scale
Note that there is no monopoly rent in these cases - outputs are not being
5
artificially constrained . Heterogeneity in the level of output is the sole
requirement needed to generate this result. Dynamic explanations, such as first
mover advantage or lags in imitation, are possible explanations of
heterogeneity in output (and are explored in more detail later in this section).
Other benefits also accrue to large firms. Recently, the state government
of Victoria issued a tender for a single casino licence in Melbourne. Only three
consortia made bids for the licence Why did this occur when the casino offered
the promise of large guaranteed profits? The answer is that, much like a game
of poker, the stakes to enter the game were too high for most players. The final
licence fee sold for $170 million with an estimated $500 million needed to
build the casino complex. Porter (1980) lists capital requirements as a barrier to
5
Limit pricing would occur if firm B reduced its price below Ca forcing firm A from
the market and constraining demand to Qb. The subsequent rents would be monopoly rents.
24
entry. However, “access to capital” can also be treated as a scarcity rent that
accrues to large organisations because it creates the ability to respond to a
greater number of profitable opportunities.
It is also the case that large institutions are easier to deal with than
smaller companies for a myriad of reasons (Rumelt, 1984; Rumelt, 1987).
Retail giants, such as McDonalds and K-Mart, lower consumer search costs by
marketing a familiar range of products in a wide range of accessible locations
at steady prices. Brand awareness is created and sustained by nation-wide
advertising on a scale unobtainable by smaller firms (Porter, 1980). Nationwide buying power also ensures that margins are healthier than smaller
competitors or that savings are passed on in the form of lower prices. In the
process of establishing a national (or global) presence, large companies can
also gain a reputation for convenient and reliable service that helps to retain
customers and gain new business.
In certain industries, such as computing and telecommunications,
network economies can create very powerful competitive advantages for large
players (Rumelt, 1987).
In cases where consumer utility is an increasing
function of a firm or technology‟s market share then a slight advantage in
initial market share will result in market domination and the locking out of
competitors. The effect is strong enough to lock out superior technologies from
the market (Arthur, 1988). Positive feedback or network economies have been
observed in telecommunications networks (Brock, 1981), computer operating
software, typewriter keyboards (David, 1985), and video player standards
(Arthur, 1988). For example, the utility of owning a telephone increases in
direct proportion to the number of other people with telephones that you can
talk to. This fact, combined with the high cost of deploying telephone lines,
resulted in the domination the US telephone system by AT&T and the
establishment of government monopolies in most other countries (Brock,
1981).
25
A closely allied concept to network economies is that of switching costs
(Porter, 1980). An investment in one form of technology often requires coinvestments in other related assets. The purchase of computer hardware, for
instance, requires related investments in training, software development and
communications infrastructure. The benefits of switching to a competitive
product must outweigh not just the costs of new hardware but also the attendant
reinvestment in related assets. The old adage “No-one was ever fired for buying
an IBM” reflected the lower risk of incurring switching costs if one stayed with
the market leader.
Information
It is argued that information asymmetry among competitors also plays an
important role in preserving firm heterogeneity. The term, causal ambiguity,
has been coined to refer to a lack of information on which resources to imitate
to create a competitive advantage (Lippman & Rumelt, 1982; Rumelt,
1984).“When the link between a firm‟s resources and its sustained competitive
advantage are poorly understood, it is difficult for firms that are attempting to
duplicate a successful firm‟s strategies through imitation of its resources to
know which resources it should imitate” (Barney, 1991, p. 109).
In some cases, it may be possible to identify the causal factors underlying a
competitive advantage and still not have sufficient information to duplicate the
strategic assets. Socially complex resources such as a firm‟s reputation or
corporate culture may play an appreciable (and identifiable) role in generating
above-average performance but resist analysis and systematic efforts to imitate
them (Dierickx & Cool, 1989; Schoemaker, 1990). This problem may be
summarised by the remark „We know what to do, but we don‟t know how to do
it‟.
Schoemaker (1990) characterises the strategic problem as the ability to
partially analyse the complexity arising from uncertainty. When uncertainty can
be completely resolved, no strategic behaviour will occur - all firms will act to
26
maximise their interests. If uncertainty is too high, the task of analysing the
situation becomes too complex and results will be due to luck. In Schoemaker‟s
view, strategic behaviour occurs when a situation is complex enough to generate
a range of views on which actions will be successful; creating heterogeneous
outcomes in performance.
One solution to the problem of complexity is to hire employees from
successful firms in the hope they might be able to transfer complex, or hidden,
strategic assets. This process will be frustrated if the knowledge is tacit. Tacit
knowedge is a set of rules created by doing that cannot be usefully explained to
others with no experience of the skill (Nelson & Winter, 1982; Polanyi, 1962;
Teece, 1982; Winter, 1987). The classic example is riding a bicycle; a skill that
cannot be taught in theory but must be experienced. Every profession has
aspects of learning by doing and this acts as a barrier to entry to many
businesses.The implication is that a competitor will find it hard to duplicate a
skill that cannot be codified.
It is also feasible that the owner of an asset could be completely ignorant
how the asset was created. For instance, the asset may have been accidentally
created or the original creator may have left the company and left no record of
the process. Similarly, the asset could have emerged from the complex
interactions of members of the organisation. Consider an ant colony. It makes
no sense to acquire an ant from an ant colony and expect it to duplicate the
foraging behaviour of the nest. The foraging skill is innate (tacit?) in individual
ants but the effect of the whole is greater than the sum of the parts. In this
respect, even the most senior executives may have little detailed knowledge of
how firm routines actually operate to create a competitive advantage.
We have already discussed how large firms can provide convenience for
customers by reducing search costs. Information asymmetry also creates search
costs for firms wishing to imitate competitors. The costs of acquiring, analysing
and acting on information about the strategic assets of successful firms may
outweigh the (potential) benefits of imitating those assets.
27
Time
An emerging, but influential, stream of research in the resource-based
literature, termed the dynamic capabilities school by Schulze (1994), has
argued for the primacy of temporal factors in the determination of resource
heterogeneity (Dierickx & Cool, 1989; Teece, Pisano, & Shuen, 1990; Teece et
al., 1994). In mainstream economic theory, actors have perfect knowledge of all
states of the world (past, present and future) and perfect knowledge of which
technologies will yield the greatest value in each state of the world.
“Neoclassical models operate in a world without secrets, without frictions or
uncertainty, and without a temporal dimension” (Rumelt et al., 1991, p. 13). In
reality, “history matters” and “a firm‟s previous investments and its repertoire
of routines (its „history‟) constrains it future behaviour” (Teece, Pisano &
Shuen, 1994, p. 24). The literature has identified as least three ways that time
protects rare resources from imitation: time compression diseconomies, path
dependency and inertia.
Not all strategic assets can be traded in markets or separated from the
firm (Teece, Pisano & Shuen, 1994). Many strategic assets, including
reputation, corporate culture and key firm routines, evolve over long periods of
time. “It takes a consistent pattern of resource flows to accumulate a desired
change in asset stocks” (Dierickx and Cool, 1989, p. 1506). Time compression
diseconomies are based on the proposition that doubling the flow of inputs that
created an asset will not lead to the accumulation of a given level of asset stock
in half the time (Dierickx and Cool, 1989). Thus if firms decide to replicate a
strategic asset they are more or less bound to follow the same timetable as the
original innovator.
The concept of path dependency is perhaps even more critical to the
inimitability of firm resources. Earlier, we discussed Schoemaker‟s thesis that
firm heterogeneity resulted from the selection of different courses of action in
the face of complexity and uncertainty. Schoemaker‟s analysis was primarily
concerned with a static view of strategic choices. By adding a temporal
28
dimension to his analysis, a situation is reached where “...not only do firms in
the same industry face „menus‟ with different costs associated with particular
choices, they also are looking at menus containing different choices” (Teece,
Pisano & Shuen, 1994, p.25). Path dependency holds that future choices are
constrained by the history of past decisions.
The existence of path dependencies increases the role of commitment and
the importance of strategic decisions (Ghemawat, 1991). In many industries,
once a certain path is taken, the magnitude of the commitment locks the firm
into its chosen investment strategy. For example, the world‟s airlines face huge
capital investments in aircraft and associated infrastructure. Airlines tend to
operate with an homogenous fleet composed of a small number of aircraft
types, usually purchased from the same manufacturer. There are several sound
economic reasons for maintaining this homogeneity. If a plane becomes
unserviceable a spare aircraft of the same type can be more easily substituted.
In addition, pilots and mechanics must be rated on each aircraft type. The use
of multiple aircraft types increases training costs and spare parts inventories. It
also builds inflexibility into the flight schedule as planes must be shifted to
accomodate pilots who cannot fly certain types of aircraft. As the average
service life of a jet airliner is 15-20 years, airlines tend to become locked into
their choice of aircraft type. This can have serious peformance-related
implications if the chosen aircraft type does not match the market requirements.
Consider the case where the airline buys a small number of large jet aircraft
when the market prefers a higher frequency of smaller flights. The nature of
path dependent commitments is that the firms cannot costlessly switch from
one asset position to another. The costs of changing paths are generally
significant, if not insurmountable, and have been termed quasi-irreversible
commitments in the literature (Teece et al., 1994). In the airline industry, the
costs of changing an airline‟s fleet not only include capital losses on aircraft
and parts; but also brokerage fees; changes to aircraft handling facilities; and
the retraining, retrenchment or redeployment of staff.
29
Path dependency may also play a significant role in the generation of first
mover advantages (Ghemawat, 1991; Porter, 1991). A significant amount of
evidence has accumulated to demonstrate that early entrants into an industry
often perform better than later entrants (Lieberman & Montgomery, 1988). One
plausible hypothesis for this effect may be that the actions of earlier entrants
change the „menu‟ of choices available in an industry. Earlier entrants are able
to establish dealer networks, stake out favourable locations and construct
industry standards. Maturing industries are therefore not level-playing fields all competitors do not have the same menu of opportunities. Early entrants
work to construct a set of strategic options that are favourable for themselves
and constrain the choices available to latter players. This view, implicit in
Porter (1980) and other work in industrial economics, seemingly implies that
early entrants distort the strategic choices or playing field of latter entrants and
create barriers to entry (or mobility barriers).
Inertia
“The important strategy problem facing a firm may well be internal inertia rather
than product-market conditions” (Rumelt, 1995, p.104).
Despite the evidence that a considerable number of plausible ex post
factors exist to hinder imitation, Rumelt‟s view echoes the assessment of
several influential management writers that a lack of ability to respond to
innovation may be the key determinant in maintaining firm heterogeneity
(Ghemawat, 1991; Hannan & Freeman, 1984; Prahalad & Bettis, 1986). At
one extreme, Hannan and Freeman, in their work on organisational ecology,
contend that organisations are incapable of adapting to their environment and
that inertia is the rule rather than the exception. More moderate writers have
sought to identify factors that create a propensity for inertia and are willing to
accept that firms lie on a continuum between inertia and dynamism.
Prahalad and Bettis (1986) have argued that the primary limit to diversity
(and hence imitation) in a corporation may be the top management team‟s
cognitive ability to cope with strategic variety. The „dominant logic‟ or
30
management philosophy of top managers will tend to be heavily dominated by
experiences with the „core business‟. As the corporation moves further from its
core business, the possibility of an inappropriate response increases and the
response time to new opportunities increases. Prahalad and Bettis invoke
several findings from cognitive-behavioural psychology to explain the human
tendency to limit problem-solving behaviour.
As evidenced by his earlier work on isolating mechanisms, Richard
Rumelt has a unique talent for categorising and cataloguing important factors
surrounding a strategic phenomenon. In the case of inertia, Rumelt (1995)
describes the ability to change as being constrained by five frictions (see Table
2.3). Each friction is itself merely a label for a wide range of related
behaviours. Rumelt has extended the focus beyond the cognitive factors
identified by Prahalad and Bettis (1986) by including psychological,
sociological and political causes of inertia. However, beyond a short
description of each behaviour, Rumelt does not go into any depth on the
prevalence or importance of any particular factor. As in previous work, Rumelt
presents a provocative and interesting set of factors for further discussion.
Inertia is just emerging as a significant issue in the dynamic capabilities
literature and these issues will no doubt form the basis of future research
projects.
Table 2.3 Rumelt's Five Frictions
Friction
Distorted perceptions
Dulled motivation
Failed creative response
Political deadlocks
Examples
• myopia or short-termism
• hubris
• denial
• superstitious learning
• information filtering
• grooved thinking
• direct costs of change
• cannibalization costs
• cross subsidy comforts
• insufficient speed or complexity
• reactive mind set
• inadequate strategic vision
• departmental politics
• incommensurable beliefs
• vested values
31
Action disconnects
• leadership inaction
• embedded routines
• collective action problems
• dysfunctional corporate culture
• capability gaps
Source: Rumelt (1995)
Ex ante limits to competition
Research on imitability has also considered ex ante limits to competition.
Neoclassical economics holds that if a factor of production has the capacity to
produce scarcity rents, the owner/supplier of that factor in the “strategic factor
market” would raise the selling price to completely erode its rent-producing
capacity (Barney, 1986a). Barney maintains that scarcity rents will only be
earned if a) unforeseen events in the future increase the rent-producing capacity
of a strategic asset (ie. the buyer is lucky), or b) the buyer and supplier hold
different expectations about the value of the factor (ie. the buyer has superior
information).
Barney‟s case is somewhat overstated on at least two grounds. Firstly,
some strategic assets are simply not tradeable.
“The very essence of capabilities/competences is that they cannot be readily
assembled through markets” (Teece, Pisano & Shuen, 1994, p. 14).
Assets such as a strong corporate culture or reputation tend to accumulate
over time (Dierickx & Cool, 1989) and are never openly traded on the market.
Secondly, the nature of path dependency implies that once an initial
heterogeneity is established, whether by luck or inspiration, it may be
strategically exploited and reinforced. This process can be described as the
“slippery slope” of competitive advantage. For instance, as we have discussed
earlier, Pareto rents occur when asset owners achieve a greater return in one
application rather than the next best use. For a high quality scientific
laboratory, most of the individual strategic factors - excellent facilities, good
administration, and a well-stocked library - can be acquired at market prices.
The price of hiring quality staff will be high initially but as the reputation and
32
quality of the institution grows, it will become easier and easier to acquire good
staff. Over time, the marginal cost of specialised labour will fall, or more
correctly the rent-value of specialised labour will increase. It is the synergy
between these assets that leads to the creation of Pareto rents.
Pareto rents are just one example of the rents that arise for first movers.
In the above example it is assumed that some pool of quality staff exist waiting
to be hired. In reality, it gets harder and harder to attract quality staff as an
industry matures and quality staff are locked into early mover organisations.
The cost of hiring quality staff from these institutions may eventually bid away
any Pareto rents accruing from the synergy of talent.
What does the “slippery slope” of competitive advantage really mean? It
means that once an initial heterogeneity is established, new opportunities open
up that are not available to normal competitors. In the language of path
dependency, a new “menu” becomes available that is not available to other
competitors. New sources of competitive advantage naturally arise for larger,
more successful companies. Economies of scale and scope emerge as
significant. Increased free cash flows enable new opportunities requiring large
amounts of capital (such as new factories or expensive R&D) to be exploited.
Positive feedback loops and network economies may become important leading
to de facto standards in the industry. “Success breeds success” and successful
firms have a tendency to ride down a “slippery slope” to an even more
dominant position. Barney is right to question the source of initial resource
heterogeneity but subsequent rent creation may be more attributable to Pareto
rents (ie. the exploitation of opportunities not available to others).
Evolutionary Dynamics in Strategy
All dynamic approaches are characterised by their emphasis on the role
of time as a key determinant in sustaining competitive advantage. The dynamic
capabilities school is perhaps the most developed of the non-evolutionary
dynamic approaches but the dynamic capabilities school was not the first to
33
recognise the importance of dynamic factors in strategic management. This
section is dominated by reviews of organisational ecology and evolutionary
economics. We will also examine contributions from chaos theory, the
configurational school of strategy, and other isolated contributions on nonbiological metaphors regarding strategic dynamics.
Evolutionary theories form a large subset of dynamic approaches to
strategy; an approach that seeks to establish a metaphor between biological and
organisational processes. In the Darwinian system, evolution proceeds via three
mechanisms: natural selection, random variation and retention of selected
traits. Natural selection or „survival of the fittest‟ ensures that only those
organisms that survive in their environment will reproduce and replicate. In
Darwin‟s scheme, adaptation does not occur at the level of the individual (the
phenotype) but in successive generations of an organism, that will either retain
the genetic material (the genotype) needed to survive in a particular
environment or, in the worse case, become extinct. However, survival of the
fittest ensures that the average fitness of individuals in each generation will
tend to rise.
In Lamarckian evolution, the phenotype is capable of adaptation and of
passing traits learned via adaptation on to the genotype. Although Lamarckian
evolution has been thoroughly discredited in modern biology it may still play
an explanatory role in social and economic evolution (Winter, 1990).
Unless there is some way to vary the genotype, organisms would become
overspecialised and susceptible to sudden changes in their environment.
Variation ensures there will be differences among genotypes in a population. In
higher level animals, variation occurs via sexual reproduction, where half the
genetic material from each parent is combined in the child. Sexual reproduction
leads to relatively small variations in the genotype. Mutation, or the
introduction of totally new genetic material into a population, is needed to
explain sudden or large changes in the genotype and the emergence of new
populations (Hodgson, 1994).
34
Evolutionary theories of the firm have, to a greater or lesser extent, drawn
on elements of Darwinian or Lamarckian evolutionary theory. Theories based
on Darwinian approaches have the advantage of being able to draw on the wide
range of conceptual and statistical tools developed for biological researchers.
Organisational ecology
6
The organisational ecology movement arose from the work of two
sociologists in the mid-1970s (Hannan & Freeman, 1977) and most closely
resembles the pure Darwinian view in the social sciences. As a group,
organisational ecologists share an interest in measuring the rate of events
within populations. Important organisational events include births, deaths, and
transformations.
Early work in organisational ecology studied the
demographics of populations of organisations. Members of a population were
said to share a similar organisational form or were affected by the environment
in similar ways (Hannan & Freeman, 1977). Subsequent work has studied the
development of populations within organisations and even the dynamics
between populations or “communities of organisations” .
“...the emerging ecological view is that [organizations] can best be studied by
examining how social and environmental conditions and interactions within and
among populations influence the rates at which new organizations and new
organizational forms are created, the rates at which existing organizations and
organizational forms die out, and the rate at which organizations change forms.”
(Baum & Singh, 1994, p.5)
Of all the various evolutionary studies of organisations, the organisational
ecology movement has probably been the most prolific in terms of empirical
studies and numbers of works in print. Two of the major driving forces behind
this productivity have been the relative ease of gathering information on
organisational foundings and mortality, and the ability to use well-developed
statistical techniques from biological ecology to analyse the data thus collected
(Singh & Lumsden, 1990).
6
General reviews of the organisational ecology literature must be heavily qualified as
the original theory of Hannan and Freeman has been subject to considerable criticism and
modification even within the ranks of organisational ecologists.
35
The price that organisational ecologists have had to pay for this analytical
convenience has been to adopt the same assumptions about organisations as the
developers of the statistical techniques originally made about biological
organisms. Genetic inheritance fixes difference between biological populations
rather absolutely (at least on an ecological time scale). A population of lions is
quite distinct from a population of antelopes. Moreover, if environmental
conditions change, a population of lions is unlikely to change into a population
of fish or birds. Thus, the changes in the vital rates of biological populations
are isomorphic to changes in the environment. “Genetic processes are so nearly
invariant that extreme continuity in structure is the rule” (Hannan and Freeman,
1977, p. 937).
In their early work, Hannan and Freeman proposed that a study of the
environment (competition) would provide a balance to work on adaptation and
allow the explicit use of biological models: “We propose to balance [adaptation
logic]...by adding an explicit focus on competition as the mechanism producing
isomorphism. In so doing, we can bring a rich set of formal models ot bear on
the problem” (Hannan and Freeman, 1977, p. 940). Subsequently, Hannan and
Freeman have created an elaborate defence of the assumption that organisations
do not change (Hannan & Freeman, 1984; Hannan & Freeman, 1989).
According to Hannan and Freeman (1984), the need for reliability and
accountability has led firms to develop stable routines, rules and procedures.
For example, the existence of standardised procedures allows less variation in
output and hence greater quality and reliability. At the same time, successful
organisations are skilled at transmitting these routines, rules and procedures
over time.
While stability and reproducibility of routines justify the benefits of
persisting with a given organisational form, the evidence that organisations can
change is too strong for Hannan and Freeman to deny it occurs. Instead, they
construct a model of “relative” inertia where organisations are essentially slow
and inaccurate in their response to environmental changes (Hannan & Freeman,
36
1989). Hannan and Freeman emphasise the difficulty in predicting uncertain
future states and the political nature of decision making which tends to
constrain rational decision making. Thus, the speed and direction of change is
essentially uncontrollable. Hannan and Freeman do not deny that some
adaptive changes will be beneficial to the organisation. However, at the
population level, all that is required to assert the primacy of the environment is
that the outcome of such changes will be random over time. Hannan and
Freeman claim that the defining question of organizational ecology is “Why are
there so many kinds of organizations?” (Hannan and Freeman, 1977, p.936),
but by the end of the their foundation article they have already answered their
own question: “...the diversity of organisational forms is isomorphic to the
diversity of environments” (p.939).
Despite claims of relative interia, organisational ecologists clearly feel
that their methodology has application to strategic management
"Analysis of evolutionary process at the population level suggests that both the
market power and resource-based views have merit, but that the prescriptive
advantages of each are difficult to untangle without focused resesearch, in part,
on the strategies pursued by firms similarly situated" (Freeman, 1995, p. 248).
The classical theoretical development in organisational ecology has
focused on changes in foundings and dissolutions as a function of the density of
an organizational population (Boone & van Witteloostuijn, 1995). As an
organisational form becomes established, social legitimacy acts to reinforce the
adoption of successful organisational forms. Thus, successful forms will be
increasingly adopted by new organisations because the form has been
legitimated within the community or social reference group. However, this
growth force is offset by the effects of competition within a particular
environmental niche. Given a scarcity of resources, competition must lead to
higher mortality rates as the density (number) of organisations grows.
Empirical evidence has been mixed for organisational ecology, strongly
supporting the theory on some points and rather inconsistent on others (Singh
& Lumsden, 1990). Strong support has been gathered for a „liability of
37
newness‟ and a „liability of smallness‟. Across a wide range of populations,
newer and smaller organisations are more likely to fail than older and larger
organisations. The finding is consistent with the increase in competitive forces
over time. In extending their argument for the role of inertia, Hannan and
Freeman (1984) attempted to explain the result in simple terms: selection
favours inert organisations, older and larger organisations show a higher degree
of inertia therefore a liability of newness and smallness would be expected.
A more convincing argument for the liability of smallness has been
presented by Aldrich and Auster (1986). For these authors, newness and
smallness are confounded effects. New organisations also tend to be small
organisations. Small organisations face a host of challenges when competing
with larger organisations: access to capital is poor; tax laws favour corporate
governance structures; government regulations have a greater impact on small
organisations; and the lack of internal promotion opportunities makes it harder
to attract quality staff (Aldrich & Auster, 1986).
A broad range of studies has explored the effect of density dependence on
mortality across the whole population. Theoretically, these studies were
looking for a positive curvilinear effect between mortality rate and density.
Prior to the establishment of a dominant successful form, failure rates should
be high. As new organisations adopt the dominant (legitimised) form, mortality
should fall, only to increase as battles over resource scarcity cause competition.
Thus the predicted effect between density and mortality follows a „U‟ shape.
The results of studies on density dependence and mortality have been
decidely mixed. In a review of findings by Singh and Lumsden (1990), results
that accorded with theoretical predictions occurred in only 7 of the 15 major
studies in the area since 1986. At least two studies showed mortality declining
at high density, while others found linear rather than curvilinear effects of
density on mortality. Hannan and Freeman (1989) have defended the theory by
stating that inconsistent results may have been caused by attenuations of the
data set. Linear effects could result from not including data on very early firms,
38
while studies showing mortality declining at high levels may be due to firms
not having entered the competitive stage of development. While this argument
is consistent with the theory, it also makes the theory virtually unfalsifiable. A
theory is not a scientific (testable) statement if there is no way that it can be
refuted (Popper, 1969).
A wide range of criticisms have been leveled at organisational ecology.
Relatively minor criticisms focus on issues such as defining a population
(Boone & van Witteloostuijn, 1995). When are organisational forms
equivalent? Does a merger or change of name constitute an organisational
death? When do organisations share the same environmental niche? How
should a conglomerate of many businesses be classified? Typically,
organisational ecologists have focused on simple organisational forms (such as
trade unions and voluntary organisations) where these issues don‟t arise. This
raises the question whether results are specific to the type of populations
studied.
One of the central tenets of classical organisation theory and strategic
management is that large organisations can shape their institutional
environment and mitigate competitive forces. Considerable evidence exists to
support this view (Perrow, 1986; Porter, 1980). Organisational ecologists
assume that organisations and organisational forms are homogeneous and that
mortality is simply a function of density dependence at a given point in time.
This has led Marshall Meyer to describe organisational ecology as “the science
of small organizations” or alternatively the “science of failing and failed
organizations” (Meyer, 1990, p. 312). This criticism has produced a tacit
acceptance by organisational ecologists that:
“...in the mix of adaptation and selection processes that influence organizational
evolution, the relative role of selection is probably less profound for...large
organizations” (Singh & Lumsden, 1990, p. 187).
The related assumption that organisational change, if it occurs at all, can
only produce random performance improvements has drawn a vitriolic
response from strategists (Bourgeois, 1984). In fact, organisational ecologists
39
are divided on this issue, with some arguing that ecological selection is the
primary manner in which organisations change (Carroll, 1988), while others see
the study of selection as complementary to a study of change and adaptation
within organisations (Baum & Singh, 1994). Curiously, the founders of
organisational ecology have shifted from a complementary (Hannan &
Freeman, 1977) to a predominantly selectionist view over time (Hannan &
Freeman, 1989). There is an emerging view that organisational ecologists must
accomodate models of organisational change and adopt some recognition of
population heterogeneity. Recent work on mass dependence of mortality
(where density is weighted by organisation size) represents a promising move
in this direction (Barnett & Amburgey, 1990).
Evolutionary Economics
7
Economists have long been interested in using evolutionary mechanisms
to explain economic development. Hodgson (1994) makes an strong case that
Darwin was influenced by the views of economists such as Adam Smith,
Bernard Mandeville and Thomas Malthus. In turn, Darwinism has influenced
the views of later economists including Karl Marx, Alfred Marshall and
Thornstein Veblen. Respected economic authorities, such as Alfred Marshall,
have publicly commented on the need for an economic biology but failed to
make a significant contribution in the area (Hodgson, 1993).
Evolutionary economics differs significantly from organisational ecology
because of the emphasis it places on the ability of an organisation to change
and adapt to its environment (Winter, 1990). Heterogeneity, rather than
homogeneity, within populations of firms is a key assumption in the field,
reflecting a Lamarckian rather than Darwinian orientation to evolutionary
processes. Our examination of evolutionary economics begins with Joseph
Schumpeter, whom many writers in the field identify as their source of
inspiration (Nelson, 1995).
7
Macroeconomic evolution is beyond the scope of this section which is primarily
40
Joseph Schumpeter
Despite being touted as the father of evolutionary economics, Joseph
Schumpeter did not wish to associate his theory with biological evolution:
“...these aspects would have to be analysed with reference to economic facts
alone and no appeal to biology would be of the slightest use” (Schumpeter,
quoted in Hodgson, 1993, p.131).
Schumpeter used the term “economic evolution” to distinguish
8
revolutionary economic innovations from incremental improvements .
"The changes in the economic process brought about by innovation, together with
all their effects, and the response to them by the economic system, we shall
designate by the term Economic Evolution" (Schumpeter, 1939, p. 86).
This fundamental distinction between evolutionary and incremental
change can be best summed up by the quotation:
“Add successively as many mail coaches as you please, you will never get a
railway thereby” (Schumpeter, 1934, p. 64).
Schumpeter had a very broad definition of innovation which included any
means of combining resources into new combinations including new products,
process innovation, new geographical markets and novel organisational forms
(Schumpeter, 1934). In the Schumpeterian scheme, the incentive to produce
new innovations was derived from the capacity to earn entrepreneurial profit
from an innovation. Entrepreneurial profits occured because of the lag between
the introduction of a new innovation and the subsequent diffusion or imitation
of the innovation throughout the economy. This cycle of innovation, diffusion
and re-innovation was termed “creative destruction” by Schumpeter (1939).
Entrepreneurs start new enterprises, earn entrepreneurial rents which decline
over time due to imitation, and are eventually displaced (destroyed) by a second
generation of entrepreneurs.
concerned with ideas that are applicable to the processes of evolution at the level of the firm.
8
In this regard he shared Marx‟s conception of economic development as a series of
revolutions (Hodgson, 1994)
41
In later work, Schumpeter distinguished between his first model of
competitive capitalism and „trustified‟ capitalism (Brouwer, 1991; Schumpeter,
1943). In trustified capitalism, which has dominated the 20th Century,
individual entrepreneurs have been replaced by large corporations with a
degree of monopoly power. Innovation occurs “in-house” in R&D facilities.
Rather than being swept away in a wave of creative destruction, trustified firms
are more likely to withstand change or even be the source of major innovation
themselves.
It is clear that Schumpeter used the term evolution as a synonym for
development or progress. However, radical (evolutionary) innovation has been
treated as a form of “economic” mutation by neo-Schumpeterian economists
(Nelson & Winter, 1982). In reality, successful innovations will diffuse through
an economic population in much the same manner as a successful species will
diffuse through a biological population. A process of natural selection is clearly
evident in Schumpeter‟s theoretical framework. This has led Hodgson (1993)
to tenatively classify Schumpeterian theory as a quasi-Darwinian approach.
Armen Alchian
Schumpeter‟s work received little attention in the years following its
publication. The mid-20th Century was the zenith of the neoclassical school of
economics; an approach heavily conditioned on formal mathematics and static
analysis. Perfect information (or at least shared expectations) by actors in the
system was one of the conditions believed necessary to achieve an equilibrium
state in this neoclassical world. Buyers and sellers were assumed to have
perfect knowledge about prices and quantities, and also perfect information on
the most efficient technology and production techniques. Rather than assuming
perfect information, Armen Alchian (1950) explored the “precise role and
nature of purposive behavior in the presence of uncertainty and incomplete
information” (p.221) In Alchian‟s scheme, uncertainty forced firms to adapt
through trial and error towards higher levels of performance in an essentially
evolutionary process.
42
Alchian formally decomposed uncertainty into two components:
imperfect foresight and the human inability to resolve complexity (Alchian,
1950). Given these forms of information uncertainty, it followed that economic
decision makers would become uncertain as to which actions would generate
success in the future. Alchian defined success as survival. Economic survival
was based on the ability to generate positive profits, or a surplus of income
over costs. Success was not equated with profit maximisation:
“...the measure of goodness of actions in anything except a tolerable-intolerable
sense is lost” (p. 219).
In place of certainty of action, Alchian believed that decision-makers
would adopt “modes of behavior” or “guiding rules of action”. These modes of
behavior had much in common with what Simon would later call heuristics
(Newell & Simon, 1972).
Alchian identified two possible modes of adaptive behavior: imitation
and trial-and-error. Imitation of successful firms
“...affords relief from the neccesity of really making decisions and conscious
innovations” (p. 218).
Imitation was a form of uncertainty (and risk) reduction that relied on
past success breeding future success. Alchian maintained that uncertainty could
also arise from complexity, and thus imperfect attempts at imitation had the
potential to result in successful (or unsuccessful) innovations. Innovation also
occurred through the second adaptive mechanism of trial-and-error as new
products and organisational arrangements were tested in the market.
“Failure and success reflects...not only imitative behavior but the willingness to
abandon it at the „right‟ time and circumstances...when conditions have changed”
(p. 218).
Alchian explicitly sought to link his views with biological evolution. He
saw imitation, innovation and positive profits as the economic counterparts of
genetic heredity, mutations and natural selection (Alchian, 1950). Alchian also
acknowledged that the environment was not static but in a state of constant flux
43
which effectively excluded the notion of convergence to a global optimum and
introduced a signficant role for luck in success (survival) and failure (death).
“...even if the actions of firms were completely random and determined only by
chance, the firms surviving...would be those that happened to act appropriately
and thus made profits” (Penrose, 1952, p.810).
In hindsight, Alchian‟s paper generated considerable discussion and
controversy in the economics discipline. Such was the threat to orthodoxy that
Edith Penrose (Penrose, 1952) felt moved to write a scathing attack on
Alchian‟s ideas; an attack that in our view was misguided and inaccurate, but
nevetheless served to sabotage evolutionary views for another thirty years.
Penrose‟s arguments were threefold. In the first, she argued that the goal
(motivation) of positive profits was not competitive enough and insufficient to
force firms to exploit opportunities that would increase profits. In the second,
she criticised evolutionary theory for having no predictive power, and with only
being able to explain success with hindsight. The third argument criticised the
relative passivity of firms in Alchian‟s model. She claimed that in Alchian‟s
view, firms were selected by the environment, but they could not “force” the
environment to adopt changes beneficial to the firm.
Penrose‟s first argument fails to grasp the intent of Alchian‟s positive
profits argument. The heading of Alchian‟s second section was entitled
“Success is based on results, not motivation”. Alchian argues that firms may be
motivated to maximise profits but survival is dependent on achieving results
(ie. positive profits). Firms prefer higher profits but never know if their actions
are optimal, hence the need to imitate better performers or conduct trial-anderror search. However, if profits are negative, firms will undoubtedly fail
(albeit not immediately). Penrose confused the goal or motive of firms with the
effect on survival of not achieving a “positive profit” result.
Similarly, Penrose‟s third argument – that firms cannot influence their
environment – rests on a notion of the environment that excludes firms and
their actions. Biologists have long recognised that species co-adapt. The Lotka-
44
Volterra models of predator-prey interactions date to 1926 (Volterra, 1926).
The economic environment is not some faceless entity that “selects” which
firms will survive. As much as survival will depend on exogenous conditions
such as consumer demand and technological development, it will will also be
partially dependent on the actions of the firm and other firms in the
environment.
Penrose‟s second argument was her most substantial criticism:
By its very nature a prediction of the kinds of firms that will survive in the long run
must take account of all the reactions and interactions that a given change in the
environment will induce. With our present knowledge this is impossible...and places
the wrong interpretation on the kind of thing the economist can do.” (Penrose, 1952,
p. 815)
The witticism “Economists have successfully predicted eight of the last
five recessions” immediately comes to mind. Surely this is the pot calling the
kettle black? It is not entirely clear that neoclassical economics has any more
predictive power than evolutionary theory. The difficulty of prediction has not
stopped biologists using evolutionary theory, nor has unpredictability cancelled
the economist‟s research programme in the neoclassical tradition. Alchian was
suggesting a new paradigm for conceptualising firm behaviour - a paradigm
that may better describe observations of firm interactions. The fact that all
reactions and interactions are important provides a ready explanation for why
forecasts are so often wrong.
Despite her hypocrisy, Penrose effectively put the development of
evolutionary economics in English-speaking countries on hold for thirty years.
It was only following the publication of Nelson and Winter‟s An Evolutionary
Theory of Economic Change in 1982 that the field gained new momentum.
Nelson & Winter
Nelson and Winter‟s work is of considerable importance to our current
research project. Nelson and Winter‟s work represents the first computer
simulation of profit-seeking behaviour at the firm level within an evolutionary
paradigm. In this chapter, we will review Nelson and Winter‟s theoretical
45
development of evolution at the firm level. In later chapters, greater attention
will be paid to the mechanisms used to operationalise their simulation model.
Nelson and Winter were disciples of Alchian; acknowledging his work as
a “direct intellectual antecedent of the present work” (p. 41). As followers of
Alchian, Nelson and Winter equated fitness with positive profits and adaptive
search formed the basis of variation in their system. They also divided search
into innovation and imitation in the same manner as Alchian.
Penrose (1952) had been critical of the definition of natural selection in
evolutionary economics. She believed natural selection had two meanings: “In
a broad sense it covers all cases of differential survival; but from the
evolutionary point of view it covers only the differential transmission of
inheritable variations...clearly the one thing a firm does not have in common
with biological organisms is a genetic constitution” (p. 811). Conscious of
Penrose‟s criticisms, Nelson and Winter extended Alchian‟s evolutionary
theory by suggesting that routines could be viewed as the inheritable genetic
material of firms.
Nelson and Winter defined routines as “all regular and predictable
behavior patterns of firms” (p. 14). The term „routine‟ was used in much the
9
same way as the philosopher Plato used the word „form‟ . Like forms, routines
were abstract entities stored in the memory of individuals. New employees had
to be taught (or experience) how to perform a firm‟s set of routines. In these
cases, the acting out of routines by earlier employees acted as a template for
subsequent replications.
Routines could also be acquired by others outside the firm but the lack of
a suitable template tended to make imitation harder than replication. In
particular, Polanyi‟s concept of tacit knowledge (or learning by doing) was
invoked to explain frictions limiting imitation (Polanyi, 1962). Imitation and
9
Plato argued we recognised that something was beautiful because we made reference
to an abstract notion of beauty. A beautiful thing is beautiful because it possesses the form or
46
replication made a crucial link to genetic heredity that was lacking in earlier
evolutionary accounts in economics. The existence of imitation also ensured
that superior routines could propagate through the population increasing the
average fitness of firms over time. Nelson and Winter saw imitation as a
particular characteristic of sociocultural evolution that was absent in biological
systems. As in previous accounts, innovation retained its equivalence with
mutation, although it assumed a deeper significance as the creator of new
routines.
Nelson and Winter saw bounded rationality as the force that ensured that
firms would continue to hold different sets of routines:
"In evolutionary theory, choice sets are not given and the consequences of any
choice are unknown...there is no choice that is clearly best ex ante...Firms facing
the same market signals respond differently...[but over time] the competitive
system would promote firms that choose well on the average and would
eliminate, or force reform upon, firms that consistently make mistakes” (p. 276).
The fact that uncertainty resulted in a fundamental disequilbrium in the
economy has led Nelson and Winter to claim a certain affinity with
Schumpeter‟s notion of creative destruction. Hodgson (1994) has rejected
Nelson and Winter‟s self-titled status as „neo-Schumpeterians‟ by arguing that
Schumpeter never saw innovation as causing continuous disequilibrium. While
innovation shifts the economy out of equilibrium, equilibrium is viewed by
Schumpeter as the natural state and the starting point for analysis. Hodgson
argues that Nelson and Winter‟s work is much closer to the writings of Hayek
and the „Austrian‟ school which have always viewed the economy as being in
permanent disequilibrium.
Geoffrey Hodgson
Hodgson‟s (1994) recent book on „Evolution and Economics‟ has been
very critical of the tendency for social scientists to equate survival through
natural selection with fitter or better performance:
quality of beauty.
47
“It is widely, but wrongly, assumed that evolutionary processes lead generally in
the direction of optimality and efficiency...that the kinds of behaviours that are
selected in a competitive evolutionary process are necessarily superior and
relatively efficient” (p. 197).
Hodgson proceeds to cite several broad arguments in modern biology
which contradict the notion that evolution leads to „survival of the fittest‟.
Hodgson‟s work is important because it introduces contemporary debates from
biology into evolutionary economics - knowledge that challenges naive views
of Darwinian evolution.
Hodgson‟s first argument simply states that natural selection cannot
strictly optimise in an economic sense because evolution necessarily implies a
variation of forms. Genetic inheritance through sexual reproduction will always
ensure that not all offspring will share the same traits. Hodgson revisits Penrose
by questioning whether organisations really have an equivalent process of
genetic reproduction.
In his second argument, Hodgson points out that “the greater density of a
given organisational form does not necessarily imply greater efficiency” (p.
202). Fecundity, or the rate of founding, is introduced as an important concept.
Hodgson argues that the observed predominance of hierarchical firms over
cooperative firms may simply reflect the ease of founding a hierarchical firm
rather than any superiority or efficiency of the form.
Path dependency forms the essence of Hodgson‟s third objection to the
efficiency of evolution. Naive evolutionists tend to believe that natural
selection would lead a system to gravitate towards the same equilibrium point
regardless of the starting position. Modern biologists have shown that even
small genetic „accidents‟ can have a major impact on the state of a system. The
„butterfly effect‟ in chaos theory also implies that chaotic systems with very
small differences in initial conditions diverge exponentially over time (Gleick,
1987).
48
Path dependency has much in common with Hodgson‟s fourth category
of lock-ins and chreodic development. A lock-in occurs when an initial
(perhaps imperfect) innovation is also associated with high switching costs.
This leads to strong positive feedback loops that „lock-in‟ the dominant
technology to the exclusion of (possibly) more efficient designs. Examples of
sub-optimal lock-ins can be found in the VCR market (Arthur, 1988) and
typewriter keyboards (David, 1985). Chreodic development implies that future
developments are predicated on past choices. At each point in time we must
choose from a range of opportunities. Each choice opens up new opportunities
but eliminates the opportunities associated with choosing a different path:
“what exists is not necessarily the most efficient, and the world could have
been otherwise” (p.206).
Hodgson is also keen to emphasise the importance of context in
evolution:
“Evolution does not generate eternal attributes or characteristics in accord with
some absolute standard of „fitness‟...what is „fit‟ is always relative to an
environmental situation.” (p. 207).
Moreover, context implies that the fitness of a particular firm may be
dependent on the environment as a whole rather than the individual
characteristics of that firm.
Imagine a three dimensional surface. On the x-axis are all the possible
variations of species A from a given starting position, and on the y-axis all the
possible variations of species B from its starting position. On the z-axis is a
measure of „fitness‟ of Species A (or B) given the joint variations of A and B.
This mapping is known as a fitness surface or fitness landscape. A „rugged‟
fitness landscape (Kauffman, 1988) is one in which a number of local optima
or peaks exist surrounded by troughs or valleys of fitness. In a rugged fitness
landscape, species may evolve around a local optimum but will be hindered
from moving to the global optimum because the path is blocked by regions of
low fitness. Thus, an evolutionary equilibrium does not necessarily imply a
global optimum.
49
Moreover, the joint actions by species (or firms) on a fitness landscape
may change the landscape (ie. the fitness landscape is not static).
“With a shifting fitness surface we often have no reason for asserting that one
„optimal‟ solution will prove to be lastingly better than another” (Hodgson, 1994,
p. 210).
Hodgson‟s final criticism of naive evolution concerns intransitivity. It is
possible to imagine a case where species A dominates species B, where B
dominates C, but C dominates A. This system would be unlikely to reach an
equilibrium, tending to cycle between periods of dominance by A, B and C. At
any cross-sectional point in time it makes little sense to ascribe success to
whatever species (or firm) happens to have the largest share at the time.
Evolutionary and Ecological Concepts in Strategic Management
The body of work on evolutionary and ecological concepts in strategic
management is considerably underdeveloped in comparison with evolutionary
economics and organisational ecology. Although early reference to „evolution‟
can be found in the work of Greiner (1972), on closer inspection he uses the
term simply to refer to periods of smooth and steady development between
„revolutions‟(Greiner, 1972). Greiner‟s model of evolution and revolution has
more in common with Piagetian cognitive development than biological
evolution. In his model, organisations must follow an invariant step-wise
progression through a number of qualitatively different periods as they grow.
The progression to a new stage is characterised by an organisational crisis
followed by resolution (or organisational dysfunction).
Bruce Henderson, the late founder of the Boston Consulting Group,
makes a case for adopting a biological metaphor in strategy (Henderson, 1989).
According to Henderson, business is an ecosystem where firms engage in a
competitive struggle for resources. Unlike biological evolution, strategic
competition compresses time. “Competitive shifts that might take generations
to evolve instead occur in a few short years” (p. 142). Echoing Schumpeter
(1934), Henderson argues that revolutionary strategic competition punctuates
50
periods of gradual evolution via natural competition. He also comments that
the periods of quiet evolution may be becoming rarer as the number of
aggressive competitors introducing new business strategies increases.
In another HBR article, James Moore puts an interesting twist on
Henderson‟s position by positing the existence of business ecosystems (Moore,
1993). A large firm, such as Ford, supports a vast network of dealers, suppliers
and advisers. Moore claims that smaller companies are co-evolving with Ford
in a symbiotic relationship; their survival being dependent on Ford‟s survival.
In turn, Ford needs to leverage the capabilities of its network in order to
enhance its own performance. This co-dependency between „species‟ Moore
calls an ecosystem. Other car manufacturers also possess ecosystems leading
Moore to claim that “It‟s competition among business ecosystems, not
individual companies, that‟s fueling today‟s industrial transformation” (p. 76).
Evolution has also been invoked in the empirical academic literature by
Barnett, Greve and Park (Barnett, Greve, & Park, 1994). The authors attempted
to distinguish between a „naive‟ evolutionary model and evolution guided by
purposive „strategic‟ action. They argued that the naive evolutionists believe
that natural selection results in organisations that are more capable and better
performing. Strategy, on the other hand, was seen to reduce competitive forces
and selection pressures (Porter, 1980). Barnett et. al (1994) focused particularly
on the ability of large diversified businesses to protect weaker subunits, leading
to a “survival of the weak” (p. 14).
Interestingly, in the results of their empirical study on unit and branch
banks (basically single and multi-branch operations), both organisational forms
were equally likely to survive. A „survival of the fittest‟ effect was found with
unit banks. The intensity of past competition increased current performance.
Surviving unit banks were perceived as more competent. With branch banks,
size was a significant predictor of performance, but was only significant when
the bank operated in multiple markets with other branch banks. This effect was
51
linked to „mutual forebearance‟, where competitors restricted punitive activities
for fear of retaliation in other markets.
On reflection, the arguments of Barnett et al (1994) also display a degree
of naivety. Membership of a branch network certainly reduces competitive
forces for each individual branch, but the positive effects of belonging to a
network at least balance, if not exceed the deleterious effects of „selection
barriers‟. This points to the importance of a holistic or meta-analytic approach
to evolution. Just because one element in a system is weak does not imply that
the whole system must be weak. In the animal kingdom, an individual human is
weak relative to powerful creatures such as lions and tigers. However, the
ability to work as a group has increased human capabilities (and thus biological
„fitness‟) a thousandfold.
Non-biological conceptions of strategic dynamics
Evolutionary and ecological concepts of strategy have the ability to draw
on a rich literature in biology, evolutionary economics, and organisational
ecology. In contrast, the literature on non-biological conceptions of strategic
dynamics has a much more limited heritage. Two intellectual movements stand
out as providing inspiration in this area: game theory and chaos theory.
Game Theory
Game theory “...is the analysis of rational behavior in situations
involving interdependence of outcomes (when my payoff depends on what you
do)” (Camerer, 1991) p. 137). Although originally used to analyse one shot
decisions between two players, game theory has now grown in sophistication to
10
consider dynamic games with multiple players :
“A repeated game consists of the repeated play of a particular game, in which the
individual repetitions are structurally independent. A repeated game is a special
case of a supergame. A time-dependent supergame is a game in which the payoff
10
including the possibility of information asymmetries between players regarding
actions and payoffs
52
of each period depends upon the actions of both the present period and those of
one or more previous periods.” (Friedman, 1990)
Friedman‟s definition of a supergame characterises the problems facing
corporate strategists quite well. For example, the dynamic capabilities school
has been particularly interested in the effects of previous actions on creating a
competitive advantage in the current time frame (Teece et al., 1994).
Given its ability to model the dynamic strategic behaviour of multiple
players in an interdependent setting, one would predict that game theory
methods would be widely applied in the study of strategic management.
Actually, only a handful of articles on game theory have been published in the
Strategic Management Journal, the premier journal in the field. Camerer (1991)
attributes this oversight to three factors. First, many strategy scholars are
ignorant of the field of game theory or lack the training necessary to apply
game theoretic models to their field. Second, most strategy research has been
aimed at finding „laws of business‟ or empirical regularities. Game theory, on
the other hand, proceeds by constructing stylised situations where the results
are highly sensitive to the assumptions underlying the game (Postrel, 1991).
Camerer (1991) also focused on the perception among strategists that
game theory required players to possess superhuman rationality. Critics of
game theory have argued that, in all but the most trivial games, the equilibrium
strategy is not obvious and requires a high degree of reasoning to obtain
(Porter, 1991). It is simply not clear that managers use this complex reasoning
when making decisions:
“I know of no instance in which the Cournot model, or even a sophisticated
variant of it, has been used in practice by the management of a real world
enterprise to guide it in making its output decisions.” (Saloner, 1991, p. 125).
Camerer counters this argument by stating that game theory works as if
players behaved rationally. He cites three mechanisms - communication,
adaptation and evolution - that push games towards an equilbrium regardless of
the level of rationality of the players. For example, pre-play communication or
„cheaptalk‟ allows players to discuss possible actions without obligation or
53
commitment and thus allows players to scope the nature of their
interdependence. Camerer admits this would not be a strong force in creating
equilbrium in a complex game.
Adaptation involves progressively improving one‟s strategy over time.
An increasing number of studies in experimental economics have demonstrated
the ability of players to learn better strategies and thus converge to an
equilibrium over time. Of course, this relies on the duration of the game and the
penalties for not reaching an equilibrium immediately. For instance, adaptive
learning is not a feature commonly associated with games of nuclear
destruction. Learning also takes time, and in some cases players may not have
reached an equilbrium strategy even after a large number of trials.
Camerer‟s argument for evolution as an equilbrium-setter draws on the
notion of an evolutionarily stable strategy (ESS) encountered in biology. In the
game of survival, the fitter species are those that select the equilibrium point
(and thus receive the highest payoff). Over time, species following an ESS will
dominate the population and creating an equilbrium at the ESS. Camerer
admits that evolution may take a long time in human games and may not be as
strong a force as adaptation.
In contrast to Camerer, Saloner (1991) openly admits that game theory has
few literal applications in strategic management. In real business situations,
executives face a large number of players with unknown payoff and action sets.
This complexity creates intractable problems for the literal game theorists.
Saloner prefers to view game theory as creating stylistic representations of
decision situations that are metaphorical rather than literal. In metaphorical
studies, researchers aim to generate general principles or surprising
(counterintuitive) results that can reframe the way practitioners view certain
problems of interdependence. Much of the popular game theory literature has
focused on communicating surprising results (Dixit & Nalebuff, 1991). For
example, the finding that tit-for-tat was the dominant strategy in repeated
prisoner‟s dilemma games was unexpected (Axelrod, 1984). While references to
54
game theory are rare in the strategy literature, Ghemawat has drawn inspiration
from several dynamic game-theoretic studies in industrial economics to write on
the nature of strategic commitment (Ghemawat, 1991).
Metaphorical results can also be subjected to empirical testing to
determine the generality of their predictions (Saloner, 1991). Saloner comments
that strategic management offers a rich source of situations for game theorists
to model and that game theorists can repay this debt by generating testable
hypotheses about strategic behaviour in stylized situations.
Chaos Theory
Organisational ecologists have argued that an inability to predict the
future will ensure that the effects of strategy on performance will be random
relative to environmental selection pressures (Hannan & Freeman, 1984).
Interestingly, support for this hypothesis has been found in the study of
complex systems in the physical sciences.
Chaos theory can be defined as “the qualitative study of unstable
aperiodic behaviour in deterministic nonlinear dynamical systems” (Kellert,
1993, p. 2). All chaotic systems are unstable, exhibiting a sensitive dependence
on initial conditions. This feature was first recognised by Edward Lorenz in
1963. While running computer simulations on a set of three nonlinear
equations, Lorenz decided to take a shortcut on data entry while attempting to
replicate a previous simulation. Lorenz inadvertently rounded off the initial
parameters from four decimal places to two (ie. 0.8872 became 0.89). Lorenz
was intrigued to find that the time paths of the equations diverged
exponentially over time (Gleick, 1987). Lorenz concluded that a sensitive
dependence on initial conditions:
“...implies that two states differing by imperceptible amounts may eventually
evolve into two considerably different states. If, then, there is any error whatever
in observing the present state - and if any real system such errors seem inevitable
- an acceptable prediction of an instantaneous state in the distant future may well
be impossible” (Lorenz, 1963, p. 133).
55
Lorenz‟s findings have led business writers to comment that:
“...if one accepts the premise that the dynamic of success is chaotic... all forms of
long-term planning are completely ineffective” (Stacey, 1991, p. 188).
In the absence of any ability to plan or control the future, managers are
then urged to develop an adaptive stance and a preparedness to react to
unexpected and unanticipated events. The term, organizational learning, is
often used to describe the process whereby groups and individuals within the
organization challenge existing mental models of behaviour and learn to
rapidly and creatively adapt to a changing environment (Senge, 1990; Stacey,
1991; Stacey, 1993).
Is the business environment a chaotic system? There would appear to be
more evidence to support the view that the business world is a complex system
poised on the edge of chaos, rather than a system in a state of perpetual chaos
(Phelan, 1995). Industries appear to go through long periods of incremental
change with the occasional discontinuous change or punctuated equilibrium
(Moore, 1993; Tushman & Anderson, 1986). Leaders in a wide range of
industries tend to hold their position for relatively long periods of time (often
for decades), suggesting that their source of competitive advantage may be
somewhat sustainable, or at least renewable (Chandler, 1990). Examples of
companies that have held a dominant position in their industry for many years
include IBM, Ford, Citibank, Exxon, AT&T, McDonalds and ICI. This is
hardly a picture of unrestrained turbulence and unpredictability that chaos
theorists have claimed exists in today's business world. Not all industries are
affected equally by these changes and different firms will go through these
changes at different times (Mintzberg, 1994).
Despite the limited applicability of the chaos metaphor, the study of
chaos (and complex systems in general) has given strategy researchers a new
toolbox with which to approach the study of dynamic processes (Levy, 1994).
Researchers in the physical sciences have demonstrated that complex dynamic
systems can be modelled with nonlinear equations. These nonlinear equations
56
are capable of displaying qualitatively different patterns of output as parameter
values are changed. Moreover, the qualitative state of the system at any point in
time may have serious implications for the study and application of strategic
management techniques.
A Note on Studying Dynamic Processes
Dynamic mechanisms are often difficult to study because the time and
cost of gathering large amounts of data over several time periods is generally
prohibitive. In addition, statistical techniques for testing hypotheses with
longtitudinal data are not readily available (Miller & Friesen, 1982). This has
resulted in researchers of dynamic processes adopting qualitative methods,
usually case studies, to study small numbers of organizations (Porter, 1991).
This, in turn, has made contingent hypothesis testing virtually impossible.
Of course, one exception to this generalization is organizational ecology
(Hannan and Freeman, 1989; Baum and Singh, 1994). By resorting to a highly
simplified model of organization-environment interaction, organizational
ecologists have alleviated the normal problems of data collection and analysis
encountered in dynamic approaches. Organizational ecology has borrowed
heavily from the biological sciences. However, as we have discussed,
biological models were developed with Darwinian theories of evolutionary
change at their core. To successfully transport these tools to organization
science requires an assumption that adaptive change by managers (ie.
Lamarckian evolution) does not increase the survivability of the firm
(McKelvey, 1994). In turn, this has made the approach of organizational
ecology unpalatable to most strategists.
Integrated Approaches to Strategy
The past decade has seen the emergence of a new research agenda in
strategic management that seeks to combine two or more of the dominant
explanations of competitive advantage into a single model (Barney, 1986; Amit
57
and Schoemaker, 1993; Schoemaker and Amit, 1994; Teece, et al., 1994;
Montgomery, 1995). The development of an integrated theory of strategy is
desirable for several reasons.
The „SWOT‟ tradition
The notion that firms should attempt to align their internal strengths to
external opportunities in the environment, while avoiding threats and
improving weaknesses, has been the dominant organizing concept in strategic
management for at least four decades (Spender, 1992). Achieving alignment, or
„fit‟, has been seen as a non-trivial task because both the firm and its
competitive environment change over time resulting in organizations moving in
and out of fit with their environment (Stacey, 1993).
The three paradigms, discussed above, clearly focus on discrete aspects
of the overall alignment concept. The competitive forces paradigm emphasises
the importance of the (industry) environment; the resource-based view the
importance of the firm; and the evolutionary paradigm emphasises the need to
maintain fit over time. Developing an integrated theory holds the promise of
merging the existing paradigms into an overarching research program that is
consistent with the archetypal conception of strategy inherent in the alignment,
or SWOT, tradition.
Empirical support for multiple effects
Support for an integrated view can also be found in empirical studies.
One of the few studies to combine sophisticated statistical techniques with
good quality data, found that all three competing paradigms contributed to
variations in firm profitability (Rumelt, 1991).
These results have been interpreted as providing strong support for a
resource-based view of the firm because differences at the business unit level
account for almost half the variation in profitability across firms (Rumelt,
1991). However, industry and cyclical (dynamic) effects are also statistically
58
significant despite being an order of magnitude lower than the business unit
effects. It is clear that these factors play an important role in determining
competitive advantage and should not be discounted in any comprehensive
discussion of strategic phenomena.
Barney
In 1986, Jay Barney introduced one of the earliest and most
comprehensive schemes for the integration of the three competing theoretical
perspectives
in
strategy which
he
termed
IO,
Chamberlinian
and
Schumpeterian. In the terminology of this chapter, these terms are analogous to
the
competitive
forces,
resource-based
and
evolutionary
paradigms
respectively.
According to Barney, new industries are defined through Schumpeterian
revolutions (Barney, 1986b). By their very nature, these revolutions will only
be partially predictable. Strategic planning can help to reduce, but not
eliminate, this basic uncertainty. A favourable position at the start of a
Schumpeterian revolution will be, to a greater or lesser extent, due to luck.
Through trial-and-error innovation, entrepreneurs will stumble upon novel
resources and capabilities that will create a competitive advantage, or earn
entrepreneurial rents.
The race is then on. Each new revolution creates new industry structures;
improving the value of various resources and capabilities and destroying the
value of others. New firms within the industry must endeavour to build (or
rebuild) valuable resources and capabilities. Barriers to entry and strategic
positioning will also occur within the industry. To the extent that the industry
remains stable (ie. is not subjected to more Schumpeterian shocks), various
strategies may be concocted using industry-based and resource-based logic.
Eventually, however, another Schumpeterian revolution will occur and the
chase begins anew.
59
Barney‟s framework provides a basic conceptual integration of the major
paradigms in the discipline. However, it does not specify any new constructs,
nor does it seek to explain in detail any of the mechanisms by which economic
rents are created. It relies heavily on the explanations of previous (competing)
theories but does not seek to explain when any particular theory is more
relevant than another. In a sense, Barney has delimited the effectiveness of the
industry-based and resource-based views to periods between Schumpeterian
revolutions but has not sought any deeper integration.
Amit & Schoemaker
Conversely, Amit and Schoemaker (1993) have proposed a theoretical
approach that explicitly seeks to integrate the competitive forces and resourcebased paradigms of competitive advantage (see Figure 2.5).
Figure 2.5 Amit and Schoemaker's (1993) Integrated Theory of Strategy
The novelty of Amit and Schoemaker‟s contribution arises from the
suggestion that the competitive forces and resource-based views are in fact two
sides of the same coin and are fundamentally interrelated. Although implicit in
the SWOT tradition this perspective had not been explicitly explored. Much of
60
their contribution comes from creating a new frame of reference that
incorporates and subsumes the terminology of each paradigm therefore
overcoming the problem of incommensurability that often prevents effective
dialog between competing paradigms (Kuhn, 1970).
Two key constructs are defined in the model: strategic industry factors
(SIFs) and strategic assets (SAs). Strategic industry factors are “the key
determinants of firm profitability in an industry [and] are determined at the
market level through complex interactions among the firm‟s competitors,
customers, regulators, innovators...and other stakeholders” (Amit and
Schoemaker, 1993: 36). The definition of strategic industry factors clearly
draws on the terminology of Porter‟s five forces model thus making the model
more accessible to adherents of the competitive forces school.
Likewise, the definition of strategic assets (SAs) draws heavily on the
resource-based language of competitive advantage. Strategic assets (SAs) are
“...the set of difficult to trade and imitate, scarce, appropriable and specialized
resources and capabilities that bestow the firm‟s competitive advantage” (Amit
and Schoemaker, 1993, p. 36).
The precise nature of the overlap between SIFs and SAs is not clear in
Amit and Schoemaker‟s presentation. One interpretation, suggested in the text,
is that both SAs and SIFs are a subset of the firm‟s resources and capabilities.
Under this interpretation, SIFs are those resources and capabilities that increase
profitability from an industry perspective, while SAs increase profitability from
a firm-level perspective. A considerable overlap between SAs and SIFs would
occur because industry profitability and firm profitability are positively
correlated.
This interpretation is contraindicated on a number of grounds. First, the
model presented in Figure 2.5 does not show SIFs and SAs arising from a
common pool of resources and capabilities. One of the underlying premises of
the resource-based view is that idiosyncratic firm resources drive (firm)
61
profitability. If all firms in an industry shared the same resources (ie. SIFs) then
it would be unlikely that those resources would also be strategic assets (SAs).
Thus little overlap between SIFs and SAs would be expected from a resourcebased perspective.
Secondly, the interpretation leaves little room for a structural explanation
of competitive advantage. All sources of profitability reduce to variations in the
stock of resources and capabilities. This betrays the language of inclusiveness
with the competitive forces paradigm suggested in the initial definition of
strategic industry factors.
A second, possibly more plausible, interpretation views strategic industry
factors as theoretically distinct from strategic assets. In this view, strategic
industry factors are the structural impediments to perfect competition that cause
variations in the strength of competitive forces from industry to industry. An
overlap between SIFs and SAs arises because certain strategic assets are needed
to exploit structural distortions in the competitive market. The following
example illustrates the key distinction between strategic industry factors and
strategic assets.
Achieving economies of scale is an important key success factor in the
capital-intensive motor vehicle industry. The marginal cost of an automobile
falls as output increases because fixed costs can be spread over more units.
However, in order to derive the maximum competitive advantage from this
relationship, a firm must possess a large production facility operating at
minimum efficient scale. Competitors with factories at minimum efficient scale
tend to outperform competitors without such facilities. The existence of
economies of scale in the motor vehicle industry is a strategic industry factor
for that industry.
In practice, strategic industry factors can only be identified indirectly by
examining the cluster of strategic assets held by successful firms. If many
successful firms in the motor vehicle industry possess large manufacturing
62
plants then we conclude that economies of scale form an important strategic
industry factor.
The view that competitive advantage arises from the alignment of
structural characteristics in the environment (SIFs) with resources and
capabilities held by the firm (SAs) provides a more active role for the
competitive forces paradigm. It also provides greater complementarity with the
SWOT tradition and appears to be supported by Amit and Schoemaker‟s
definition of SIFs as a complex interaction at the industry level. The diagram in
Figure 2.5 also portrays SAs as arising from the interaction of a pool of
resources and capabilities with a set of strategic industry factors.
Montgomery and Hariharan
Montgomery and Hariharan‟s work is interesting from the point of view
of this study because it invokes an integrated model of firm behaviour to
explain the pattern of diversification by large established firms (Montgomery &
Hariharan, 1991). At the highest level of generality, their model has the form:
P(K,J) = ? [X(K), Y(J), Z (K, J)]
where P(K,J) is the probability that firm K will enter industry J
X(K) = a vector of characteristics of firm K
Y(J) = a vector of characteristics of industry J
Z(K,J) = a vector of variables describing relationships between the
characteristics of firm K and industry J.
As a test of their model, Montgomery and Hariharan used a number of
variables to populate the various vectors. At the industry level, these variables
included growth in sales, industry profitability, concentration and R&D
intensity. At the firm level, variables included capital intensity, level of firm
diversification and sales growth. Relationship variables included the difference
63
in R&D intensity between firm and industry, the difference in capital intensity,
and the degree of relatedness of the target industry to the firm‟s existing
portfolio.
Montgomery and Hariharan found strong support in the Federal Trade
Commission‟s Line of Business Data that all three vectors of variables
positively influenced the probability of diversification. Their study represents
one of the first tests of an integrated model of strategy where success (or in this
case, the probability of diversification) relies on a fit between the
characteristics of the industry and the characteristics of the firm.
Towards a Synthesis of the Theory of Strategy
The discussions in the previous sections have served to lay the
groundwork for a discussion of the theoretical premises adopted in later
chapters of this dissertation. The theory of strategy being presented here can be
distilled into four key axioms, each of which will be discussed in turn:
AXIOMS
1. Firms as rent-seekers.
2. Strategy is a deliberate set of actions taken to create rents
3. Rent creation involves obtaining a strategic “fit” between the
organisation and its environment
4. The degree of fitness changes over time due to competitive
and other (exogenous) influences.
Firms as rent-seekers
Consistent with the direction of theoretical development in strategic
management since the early 1980s, we conceptualise firms as rent-seeking
64
entitities. Given a preference between higher or lower returns (at a given level
of risk), we assume that firms will always prefer higher returns.
A number of points of clarification are required. Previous simulation
studies in this area (Cyert & March, 1963; Lant & Mezias, 1990; Mezias &
Glynn, 1993) have assumed a degree of satisficing behaviour or inertia on the
part of management. In these models, rent-seeking or search behaviour does not
occur every round. It should be noted that our claim that firms are rent-seekers
does not make assumptions about the speed of change, it simply recognises that
managers will have a preference for higher returns and will tend to align their
firm towards sources of higher rents over time.
The characterisation of the “firm” as rent-seeker raises important
questions about who is doing the seeking. The decision to model behaviour at
the firm-level is not meant as an anthromorphisation of the firm in any way. In
the decision routines incorporated in the simulation model presented in Chapter
4, there is an explicit tension that must be resolved within each firm over the
choice of possible actions to take. The method the simulation uses to resolve
this tension is not inconsistent with the dominant logic (Prahalad & Bettis,
1986) or garbage can (Cohen, March, & Olsen, 1972) views of strategic
decision-making. Nevertheless, the decision to model decision-making at the
firm-level reflects an underlying interest with strategy content, and reflects the
interest that strategy content research has in questioning why some firms
perform better than others.
Our characterisation of firms as rent-seekers also implicitly assumes that
firms are capable of adaptation to their environment. In this sense, we reject the
assumption of organisational ecology that firm adaptation is essentially random
with respect to environmental selection. However, we also reject the
neoclassical view that firms have perfect information about choice sets and
payoffs. Thus, firms are not assumed to flawlessly optimise profitability from
period to period. Rather, firms are boundedly rational (Simon, 1957), and must
learn to adapt to their competitive environment over time. The view of the firm
65
as a boundedly rational adaptive agent is also consistent with earlier thinking in
evolutionary economics (Alchian, 1950; Nelson & Winter, 1982) and
experimental game theory (Camerer, 1991). In both cases, firms (or players) are
considered to gradually move towards equilibrium as they learn to adapt to the
„rules of the game‟.
Camerer (1991) has argued that a process of adaptation justifies
modelling strategic situations in game-theoretic terms. According to Camerer,
adaptation will lead players towards a predicted equilibrium position over time.
While we accept the principle that firms will adapt towards higher rents, we do
not accept Camerer‟s conclusion that this justifies, or even mandates, the use of
game theory for strategic modelling. The study of strategic management is
essentially the study of firm heterogeneity (Nelson, 1991). To utilise a tool,
such as game theory, which intrinsically focuses on equilibrium-style models
and symmetry of choice sets among players is to obscure the heterogeneity that
may arise in periods of adaptation. It may be that the rate of adaptation is one of
the fundamental drivers of heterogeneity (Rumelt, 1995).
Game theoretic models also use highly simplified models of reality.
Several strategists have argued that in the complexity and uncertainty of the
real world, boundedly rational managers will inevitably hold different
expectations about the future and will thus follow different strategies or paths
(Nelson, 1991; Schoemaker, 1990). This diversity of expectations will be
absent in a typical game theoretic model because the choice set is usually wellspecified and limited in scope. In real world situations the choice set is much
greater (and thus more complicated and uncertain). Nelson (1991) sums up our
position on the search for rents rather well:
“...it is nonsense to presume that a firm can estimate an actual „best‟ strategy...a
basic premise of evolutionary theory is that the world is too complicated for a
firm to comprehend...thus diversity of firms is just what one would expect under
evolutionary theory...it is virtually inevitable that firms will choose somewhat
different strategies...some will prove profitable, given what other firms are doing
and the way markets evolve, some will not. Firms that systematically lose money
will have to change...or drop out of the contest” (Nelson, 1991, p.69).
66
Strategy as deliberate and intendedly rational behaviour
In the previous section we argued that firms will adapt in different ways
because complexity creates a diversity of expectations about the future and this
diversity leads firms to adopt different strategies. We also need to understand
the process by which firms select their strategic moves, or „how‟ they adapt.
For instance, organisational ecologists argue that organisational change is
essentially random relative to environmental selection pressures. Similarly, our
discussion of evolutionary theory has highlighed luck or „accidents in history‟
as key determinants of survival in the game of life (Hannan & Freeman, 1989;
Hodgson, 1994).
If organisational success is simply the result of luck or initial
endowments then there would be little need for a theory of strategy (Barney,
1986a). While it is recognised that luck may be the cause of some business
successes, we categorically reject the notion that random actions by firms are in
any way strategic. As we have argued in the opening sections of this chapter,
strategy is a deliberate set of actions taken to create rents. Similarly, while
Mintzberg may describe any pattern of consistent actions as an „emergent‟
strategy, we would argue that the label of strategy should be only applied in
those cases where actions are driven by a conscious intent.
Naturally, strategy researchers are keen to downplay the role of luck in
determining success, but it is also clear that firms can make maladaptive
changes or even fail to change at all (Rumelt, 1995). A theory of adaptation
must be able to explain failure as well as success.
Complex problem-solving behaviour has been extensively studied by
psychologists and cognitive scientists. Games, such as chess, have been a
favourite area of study, with chess being jokingly referred to as the Drosophila
11
of cognitive science. Chess is fascinating because it is a sequential game with a
11
Drosophila , or the common fruit fly, has been extensively studied in genetics
67
well-defined algorithm
12
for finding a solution (Egidi & Marris, 1992).
Unfortunately this algorithm requires a complete exploration of all strategies some 10120 combinations. Despite the existence of a solution algorithm, expert
chess players do not use the algorithm or even a variant of it to win a game.
Experts use rules of thumb or heuristics to selectively search the set of possible
solutions (Chase & Simon, 1973).
Simon has generalised the findings in chess to four basic observations
about human problem solving (Simon, 1992). Firstly, most problem solving
involves selective search through large spaces of possibilities. The selectivity,
based on rules of thumb or heuristics, allows such searches to reach success in
a reasonable length of time, where an undirected trial and error search would
require an enormous time, and often could not be completed in a human
lifetime.
Secondly, some heuristics are specific to the task domain while others are
general. General heuristics are weaker than specific heuristics. We tend to fall
back on weak (general) heuristics in the absence of specific heuristics. The
most important and widely used problem solving heuristic is means-end
analysis. It involves comparing the present situation with the goal situation and
noting the difference. This cues memory about possible operators that could be
applied to resolve the difference.
Operators, or productions, are rules for manipulating the environment to
effect a solution. Usually of the form, if condition then action, a human expert
possesses about 50,000-100,000 of these operators for a specific problem
domain. In general, the effectiveness of problem solving is based on the degree
of recognition (the if-condition) and the number of available operators in
memory.
These general insights on human problem solving have enabled
researchers to construct computer-based replicas of human experts . These
12
the Von Neumann algorithm
68
“expert systems” have formed a large class of study in the emerging field of
artificial intelligence. Expert systems have been constructed for a wide variety
of domains, ranging from chess (Newell & Simon, 1972) to organisational
design (Baligh, Burton, & Obel, 1986; Blanning, 1991).
We have defined the strategy problem as the search for economic rent in
a competitive environment. The problem is highly complex because of the
extensive range of possible moves and counter-moves. Few would dispute that
the number of corporate strategy combinations could be expected to exceed the
10120 chess combinations by several orders of magnitude.
The complexity of strategy-making strongly suggests that managers will
attempt to approach the problem heuristically. In fact, research findings in
strategy (such as Porter‟s five forces model and the resource-based view) could
easily be classified as general (weak) heuristics for rent creation. Although
opening and closing routines in chess can be reduced to specific heuristics, the
mid-game is often very fluid and creative with only general heuristics being
available. Strategy research is thus unlikely to reduce the degree of firm
heterogeneity by revealing general principles. Creativity and originality will
remain important.
Strategy process researchers have become increasingly adept at mapping
strategic thought processes (Huff, 1990). Schoemaker (1990) has argued that
heuristics are also important for normative models of strategy (refer to Figure
2.6). At low levels of complexity, resource allocation decisions are simple and
transparent. This is essentially the neoclassical position; information is cheap
(or free), and choices and utilities can be easily determined. At the other end of
the complexity spectrum, certain decisions will be so complex that strategies
are essentially worthless and outcomes will be random (or due to luck). We can
recognise this as the central assumption of population ecology.
At moderate levels of complexity, managers will begin to use heuristics
to simplify problems. Because managers will not use the same heuristics,
69
differing expectations as to which resources will yield economic rents creates
differences in asset ownership. The observation that the sale of assets in
strategic factor markets for less than their rental value could only occur due to
luck or differing expectations (Barney, 1986a) is congruent with Schoemaker‟s
argument. In Schoemaker‟s approach, information asymmetry arises from the
13
operation of disimilar heuristics in strategic problem solving . In turn, the
evolution of various heuristics may arise from the different experiences,
histories and cultures of competing firms (Prahalad & Bettis, 1986). Although
heuristics are not guaranteed to produce solutions (being approximations rather
than laws) the employment of heuristics in strategic problem solving is both
deliberate and intendedly rational, thus meeting our definition of strategic
14
behaviour .
O pportunity
for
Re nt C re ation
Low
(Neoclassical)
.
Medium
(Strategic Mgt)
High
(Pop. Ecology)
C omple xity
Figure 2.6 Relationship between Complexity and Rent Creation
Rent as strategic “fit”
As we have seen in earlier discussions (cf. 2.6), strategy has been heavily
dominated by the concept of a „fit‟ between the internal characteristics of the
organisation and the external environment. Two of the integrated theories
13
Schoemaker (1990) also argues that asymmetry is compounded by irrational
behaviour or “variable rationality” due to known cognitive biases in human psychology.
14
see Chapter 4 has a more extensive discussion of learning and adaptation
70
reviewed in Section 2.6 operationalised SWOT as a multidimensional bundle
of characteristics. In the case of Amit and Schoemaker (1993), internal
characteristics were referred to as strategic assets (SA) with strong linkages to
the resource-based view, while external attributes were dubbed strategic
industry factors (SIF) and linked to Porter‟s industry-based view. Montgomery
and Hariharan (1991) have gone a step further and introduced an interaction
term in addition to vectors of internal and external characteristics. In Amit and
Schoemaker‟s terminology, this interaction term represents a SIF x SA
relationship. The presence of an interaction term suggests that a given strategic
asset will only be valuable (ie. increase fit) in the presence of a relevant
strategic industry factor.
The implication is that the value of a strategic asset must be contingent
on its degree of interaction with environmental conditions. Achieving an
optimal fit would thus involve searching for the bundle of strategic assets that
best aligns with the bundle of strategic industry factors active in the firm‟s
environment. It is also possible to talk of degrees of fit. If firms are engaged in
heuristic adaptive search as outlined in the previous section, then the optimal
bundle of strategic assets will not be automatically acquired. Firms must learn
which strategic assets improve performance. An adaptive search process will
also see heterogeneous expectations about the value of a given strategic asset
and thus firm heterogeneity in the degree of fit and, ultimately, performance
15
(Schoemaker, 1990) .
Of course the strategic problem extends beyond simply choosing the best
bundle of strategic assets. The selection of resources also occurs against the
backdrop of competition for those resources. The resource-based view of
strategy predicts that the value of an asset will increase in direct proportion to
its scarcity. If two competitors hold the same strategic asset then the value of
the holdings will be reduced. Game theory provides a useful framework for
conceptualising competitive problems of this nature. The payoff matrix of a
15
The SIF x SA interaction is a critical driver of performance in the simulation model
71
simple competitive game is depicted in Figure 2.7. The payoff to each
competitor is simultaneously and jointly determined by the actions of each
respective competitor. In this particular game, there is no obvious dominant
strategy, and the game quickly deteriorates into “If I know, that you know, that
I know...” round of circular reasoning.
Figure 2.7 Competition for Resources
When the number of strategic choices is large, the payoffs for a single
competitor may also be represented as a fitness landscape (Kauffman, 1988). A
„rugged‟ fitness landscape is one in which a number of local peaks or optima
exist. A simple example of a rugged fitness landscape is presented in Figure
2.8. Fitness landscapes are useful as a metaphorical and pedagogical tool. A
topological metaphor of strategy enables as to conceptualise the fact that any
incremental process aimed at increasing fitness (organisational or biological)
will not reach a global optimum if it starts in the region of a local optimum and
is surrounded by fitness troughs or valleys. For example, there is a local
optimum in the lower right of Figure 2.8. This local peak is separated from the
developed in Chapter 5.
72
d Fitness Landscape
ito r A
global peak by a deep valley, thus we would expect a number of subpopulations
within this fitness landscape.
Figure 2.8 Sample Fitness Landscape
A number of theories in strategic management have intuitively supported
the observation that firms cluster in discrete arrangements. For instance, the
theory of strategic groups predicts that „mobility barriers‟ will create
heterogeneity within an industry (Caves & Porter, 1977). Porter‟s (1980) three
generic strategies of differentiation, cost leadership and focus also implicitly
acknowledge the presence of multiple equilibria.
Ghemawat (1991) has argued that strategic commitments lock a firm into
a given course of action and lock it out of others. Lock-ins and lock-outs are
consistent with the theory of fitness landscapes. Given that firms can choose to
73
start in different regions of the fitness landscape, firms will be locked-in to that
region of the landscape that is bounded by lower areas of fitness. Thus, in
Figure 2.8, firms on the right and left of the fitness trough are effectively
locked-in to their respective regions (unless they are willing to suffer the pain
of significantly lower returns that would result from attempting to change their
position on the landscape).
The concept of lock-in and lock-out raises important questions about an
organisation‟s ability to change. Commentators have been quick to attribute a
lack of change to organisational inertia (Hannan & Freeman, 1984; Rumelt,
1995). However, on a rugged fitness landscape this would be rather unfair to
the organisation. The two-dimensional representation in Figure 2.9 has taken
the three-dimensional fitness landscape from Figure 2.8 from the perspective of
a single firm.
B r e a k e ve n
Line
s io n a l fit n e s s la n d s c a p e
Figure 2.9 Two-dimensional fitness landscape
Imagine a firm that has decided to occupy the left peak and at some later
time finds that the fitness level of that position is falling. Also imagine if the
firm is only allowed to adapt incrementally to regions of higher fitness. It
74
follows that the peak must fall all the way down to the level of central trough
before the organisation can migrate across to the right peak. However, the
central trough is well below „sea-level‟ or the breakeven level of fitness.
Consequently, the firm will choose to hold its position, even to the point of
failure. This result is independent of the firm‟s willingness to change.
The fitness landscape view of strategic fit also has much in common with
the configurational theory of organisations (Meyer, Tsui, & Hinings, 1993;
Miles & Snow, 1978; Miller & Frisesen, 1977). The central proposition of the
configurational view is that the numerous dimensions of organisation (such as
environment, strategy, structure, culture, processes, and beliefs) cluster together
into distinct categories called configurations, archetypes or gestalts (Meyer et
al., 1993). Configurational theory is in direct opposition to contingency theory
which tends to treat organisations as “loosely coupled aggregates whose
separate components may be adjusted or fine-tuned incrementally once weak
constraints have been overcome” (Meyer, Tsui and Hinings, 1993, p. 1177).
For configurational theorists, the number of choices of strategy and
organisational form are limited by the tendency for organisational attributes to
occur in patterns: “the set of possible combinations is infinite but...just a
fraction of the theoretically conceivable configurations are viable and apt to be
observed empirically” (Meyer, Tsui and Hinings, 1993, p. 1176). In a rugged
fitness landscape, the existence of a finite number of fitness peaks would
suggest that organisations tend to cluster around those peaks. The number of
available strategies at a given point in time is therefore likely to be finite.
Whereas contingency theorists tend to see change as incremental and
relatively easy, configurational theorists see change as extremely difficult as an
organisation must change several interdependent variables, such as strategy,
structure and processes, at the same time. Thus, change is likely to be episodic
and frame-breaking; long periods of stability being punctuated by sudden shifts
in configuration. The nature of change within a fitness landscape metaphor can
be both incremental and radical. Organisations clustered around any given peak
75
will vary in their efficiency and effectiveness. To the extent that organisations
are still able to climb to higher levels of fitness, further improvement through
incremental change remains possible. However, the theory also recognises that
it is difficult to change initial positions. The existence of valleys of fitness
effectively constrains firms from easily changing peaks. While such changes
are not impossible they are likely to only occur infrequently and involve a
massive dislocation of organisational resources and capabilities.
Finally, as with the fitness landscape view, configurational theorists
equate the level of fit with performance (Doty, Glick, & Huber, 1993). They
also adhere to the principle of equifinality, or the notion that different forms
can be equally effective (Miles & Snow, 1978). The presence of multiple
peaks, and therefore multiple sources of viability, suggests that the principle of
equifinality also holds in a fitness landscape world.
Fitness as a dynamic process
Our discussions to date have assumed that firms are faced with choices in
a static invariant fitness landscape. In reality, both the competitive environment
and the firm‟s internal capabilities are in a state of flux. These changes are
continuously shaping the topology of the fitness landscape. Kauffman (1988)
has termed this phenomenon the „dancing‟ fitness landscape. Several
observations can be made about the nature of change in dynamic or „dancing‟
fitness landscapes.
As we have assumed that firms are boundedly-rational rent-seekers and
that rent can be equated with the degree of fitness on a fitness landscape, it
follows that firms will seek to adapt to higher regions of fitness over time. In a
boundedly rational world, particularly when the fitness landscape is being
deformed by competitive actions, firms will never know when they have
reached the top of a fitness peak and can be expected to search for better
positions even when fitness has been apparently maximised.
76
Exogenous or competitive factors may also cause the emergence of new
peaks in the landscape. Despite the attractiveness of new opportunities, many
firms will be precluded from moving to these higher areas of fitness by valleys
or troughs between their current position and the desired location. The view
that „ridges‟ or „saddle points‟ may provide evolutionary paths from one area of
fitness to another may be also prove a useful analogy in these cases.
While change invariably causes new peaks and ridges to arise, it also has
the potential to destroy existing areas of fitness. Schumpeter‟s (1943) notion of
„creative destruction‟ appears directly relevant here. Innovators create
entrepreneurial rents (ie. fitness peaks) which are then eroded by imitative
actions (the height of the peak falls) and is eventually destroyed by a new
innovation (the peak disappers altogether). Thus, the search for incremental
improvements is predicted to continue as long as the fitness of a particular peak
or region does not fall below some breakeven point.
The rise and fall of fitness regions has a strong affinity with the
configurational notion of punctuated equilibrium(Tushman & Anderson, 1986).
While it cannot be categorically stated that a given fitness peak will persist for
any length of time, empirical evidence would seem to suggest that firms go
through long periods of incremental adjustment punctuated with short periods
of radical change. These radical changes may be the result of technological
innovation, political events or social upheaval. Similar sentiments were
expressed by Barney when he conceptualised periods of normal competition
(on the basis of industry-based and resource-based strategies) being interrupted
by Schumpeterian shocks. These shocks fundamentally altered the basis of
competition (Barney, 1986b). Interestingly, many diverse natural systems, such
as earthquakes and sandpiles, have been observed to follow an inverse power
law, where a large number of small disturbances are interspersed with a smaller
number of large disturbances (Bak & Chen, 1991).
Critics of evolutionary approaches to economics and strategy have argued
that the approach is fundamentally flawed because it (allegedly) views the firm
77
as a passive entity whose fate is determined by changes in a faceless
environment (Knudsen, 1995; Penrose, 1952). For the record, we categorically
reject the view that the firm has no role in shaping its own competitive
environment. Clearly, the fitness landscape will change as each firm changes its
posture vis-a-vis the environment (which includes other competitors). Such is
the nature of this change that, over time, no two firms will end up facing
exactly the same fitness landscape. One of the criticisms of game theory has
been that competitors often have symmetric choice sets and payoffs (Postrel,
1991). The fitness landscape metaphor does not share this complaint.
We have already discussed how firms starting on, or around, different
fitness peaks are locked-in to their decisions. This implies a certain path
dependency in strategic decision-making; a path-dependency predicated on a
firm‟s initial starting position. But path dependency does not just arise from
different initial starting points. Each time a firm makes an irreverisble (or
quasi-irreversible) commitment to a given action rather than its alternative, the
world is changed forever. Hodgson (1994) has used the term chreodic
development to describe how different states of the world can arise from
decisions to branch down one path rather than another. The location of a
particular firm in a fitness landscape and the shape of that landscape are heavily
dependent on the history of prior choices of the firm and other actors in the
landscape. Put simply, history matters.
Summary
This chapter opened with a discussion on the nature of strategy. Strategy
was defined as an intended, but contingent, set of actions taken to achieve a
particular goal or objective. The development of heuristics or „rules of thumb‟
for reacting to changed or unplanned circumstances was also considered a
strategic behaviour. In line with recent thinking in strategic management, the
firm was conceptualised as a rent-seeking entity, thus making the goal of
strategy to maximise economic rents.
78
The following three sections (s2.3-s2.5) presented an extended review of
the competitive forces, resource-based and evolutionary dynamics approaches
to rent creation respectively. This review was followed by a discussion of
recent attempts to integrate the three explanations of success into a grand
theory of strategy (s2.6).
The chapter concluded with a discussion of the theoretical principles
adopted for the current study. The concept of a fitness landscape was
introduced as a metaphorical tool to aid in the conceptualisation of some of the
central issues in strategy. Higher performance was conceived to result from
occupying higher levels of fitness in a fitness landscape. Fitness was conceived
as the fit between an organisation‟s strategic assets and strategic industry
factors in the environment.
Achieving fit was seen as problematic because the fitness landscape was
constantly being deformed by the joint actions of the firm, its competitors and
exogenous environmental changes. Moreover, at any given point in time, firms
were uncertain about the precise bundle of strategic assets required to
maximise fitness, leading to search behaviour that was both adaptive and
evolutionary in nature. The process of evolutionary change in a fitness
landscape provides a useful explanation of dynamic strategic phenomena such
as lock-ins (Ghemawat, 1991) and path dependency (Dierickx & Cool, 1989).
79
Simulation as Method
“I do not pretend to explain gravity”
- Sir Issac Newton, Principia Mathematica
This chapter aims to introduce the reader to the notion of simulation as a
valid research method for the study of strategic management. We begin with a
broad definition of simulation and then introduce several common arguments
used to justify the use of simulation as a research tool. Various approaches to
simulation are then delineated, leading to a rationale for the approach adopted
in the current study. Finally, the limitations of the simulation approach are
acknowledged.
What is a simulation?
A simulation is a dynamic representation of reality used to educate,
entertain or explain. This definition encompasses a wide range of phenomena,
from video arcade games and role-plays, to engineering models and artificial
life. Simulation is often used when real-life experiments are costly, timeconsuming or dangerous. In other cases, such as economic policy and warfare,
real-life decisions are irreversible (and consequential) ensuring that decisions
cannot be re-run after an event. In these cases, simulation provides an
opportunity to test alternative theories and courses of action in a world without
consequences. Simulation has thus become an accepted technique in a wide
range of disciplines including biology, engineering and economics.
Computer simulation involves specifying the state transition functions of
a dynamic model in a computer program. The program instructs a digital
computer to create and modify representations of the elements in the model.
Early writers on computer simulation recommended the development of a wellspecified mathematical model before commencing the task of coding the
computer program (Naylor, 1971). However, it is now recognised that modern
computers are also capable of the symbolic manipulation of knowledge
80
utilising artificial intelligence techniques. Computer simulations do not
intrinsically need to be numerically based (Carley & Prietula, 1994; Doran &
Gilbert, 1994).
Why use simulation as a research method?
Two main justifications have been given for the use of simulation as a
research method. The first focuses on the limitations of the natural laboratory,
while the second addresses the advantages of simulation theorising over prose
and mathematical forms.
Overcoming the limitations of the natural laboratory
The successful application of the experimental method requires three
conditions: observation, systematic manipulation of independent variables and
replication.. Firstly, the scientifc method assumes that the variables of interest
are (directly or indirectly) measureable (Godfrey & Hill, 1995). Second, the
variables hypothesised to have a causal effect on the dependent variable of
interest must be systematically manipulated, while other potentially influential
factors are rigorously controlled. In this manner, the contribution of each
variable can be uniquely identified.
A necessary condition for the systematic manipulation of independent
variables is the ability to replicate an experiment and attribute any change in
outcome to a change in the independent variable (other causative factors being
controlled). Ideally, other scientists should also be able to replicate reported
findings. This considerably strengthens the objectivity of the scientific method.
Strategic management, like many other sciences, has trouble meeting
each of these core requirements. Take measurement. Gathering data on
strategic behaviour has always been problematic. Firms are very reluctant to
reveal their strategies to researchers before they are implemented. Senior
managers also have a tendency to say one thing and do another leading to the
possibility of an incorrect assignment of strategic behaviour on the basis of
81
verbal testimony (Kotter, 1982). There are also problems in determining a
firm's strategy after the event. Managers can commit fundamental attribution
errors (Ross, 1977). If a firm is successful, the success is often attributed to
elements within the firm (eg. its strategy, CEO or managerial ability), while
failure is usually attributed to factors beyond the firm‟s control (Bateman &
Zeithaml, 1989a; Bateman & Zeithaml, 1989b). Reliability may also be low,
with different members of the firm ascribing success to different factors.
Therefore it is not surprising that researchers in strategic management have
often been forced to measure more accessible and reliable attributes (such as
market share, structure or R&D output) in order to infer an underlying firm
strategy. Recognition of this measurement problem has caused some strategists
to define strategy as any pattern in a stream of actions (Mintzberg, 1987).
The problem of not being able to directly observe the phenomenon of
interest is not unique to strategic management. A whole school of psychology
has committed itself to developing theories on the basis of observed behaviour
(Skinner, 1966). Extreme behaviourists dispense with the need to study or
theorise about internal thought processes altogether (despite their immediate
personal evidence of their relevance). Similarly, the industrial-organization
(IO) school of economics affords little credit to management in the
determination of corporate profits. In the IO paradigm, the structure of the
industry determines firm conduct (strategy) and hence performance. This
conveniently removes the need to observe firm strategy in favour of (more)
easily measured industry variables.
It is our contention that further theoretical development in strategic
management will necessarily be hampered without the development of a
methodology to directly study the effect of firm strategy upon performance.
The use of qualitative methods allows the researcher to establish a position
within the company conducive to gathering evidence both on strategic
behaviour and performance issues. While qualitative methods may be
instructive with regard to the insights that they are able to generate, they are
82
also extremely expensive to conduct both in terms of time and money, and can
cover only a very few possible situations. From the perspective of the scientist,
the lack of generalisability in qualitative methods is only surpassed by the lack
of replicability of case studies and the subsequent claims of experimenter bias
that this can create. The use of multiple sources (eg. interviews, public reports)
and the "triangulation method" can minimise but not completely eliminate
these criticisms.
The classic scientific method also assumes that subjects can be randomly
assigned to each level of treatment. The effects of non-controllable factors are
thus averaged across treatment levels in large populations. One or more groups
are then subjected to experimental treatments, while another group receives no
treatment (the control group). Measurements of the dependent variable(s) are
taken both before and after treatment. The experimenter also ensures that no
change occurs to any of the groups between measurement periods that may
account for changes in the dependent variable (beyond the effect of the
treatment variable).
The experimental method is alien to many researchers in the social
sciences, including researchers in corporate strategy. It hardly needs mention
that firms cannot be randomly assigned to treatment conditions. Thus, the
effects of any experimental variable cannot be clearly delineated from other
causal factors that may covary with firm profitability.
The researcher is also not free to manipulate the corporate strategy of
firms in a study, nor control any other variables of interest such as structure,
management style or corporate culture. Unlike the chemist, who may freely
manipulate variables such as temperature, heat and light, the researcher in
corporate strategy is relegated to the role of observer.
The inability to manipuate variables of interest has led to a
preponderance of ex post facto and correlational studies in the discipline. In an
ex post facto study, two or more groups are compared after an event of interest
83
has occurred. The researcher has no control over assignment to groups or the
nature of the treatment. A typical example might involve comparing the
performance of firms that take a particular strategic action (eg. vertical
integration, diversification, internationalisation) versus firms that do not. While
the researcher may go to some effort to match firms in the two groups, the lack
of random assignment and control threatens the internal validity of the study.
Interestingly, the statistical tests do not vary between experimental and pseudoexperimental designs, leading to a tendency to attribute a scientific status to the
results.
Correlational studies are much more common in the field. In this
approach a dependent variable is regressed on one or more independent
variables. The central issue lies in the fact that causation implies correlation
while correlation does not imply causation. Hidden variables and problems in
determining the direction of causality tend to plague correlational studies
(problems that are significantly reduced in a true experiment).
Replication is also problematic. Recent research on chaos theory and
complex systems has revealed that systems comprised of many interacting
agents each capable of adapting to the behaviour of others in the system show a
sensitivity to initial conditions that makes exact replication of past events
virtually impossible (Gleick, 1987; Kellert, 1993). There is some evidence to
suggest that economic systems also display a sensitivity to initial conditions
(Levy, 1994). While identifiable patterns of behaviour may exist even minor
changes in initial conditions may have very different outcomes (the so-called
"butterfly effect"). The upshot is that even in the highly unlikely event that a
researcher could convince firms to re-enact their strategic actions, small
differences between the two studies that are beyond the researcher's control,
could destroy the validity of the study.
The use of a simulation model allows the researcher to create multiple
microworlds upon which to experiment. “The researcher is able to control all
the variables under consideration, manipulate them to uncover their effects on
84
dependent variables over time, examine all possible combinations and
interactions of variables, and examine the dynamic effects of the variables”
(Lant, 1994, p. 196). In addition, input, process and output variables can be
measured with a high degree of accuracy and reliability. Thus, the messiness of
the real world is replaced by a pristine artificial world where the experimenter
1
has a high degree of control over the interactions that occur .
A simulation is also capable of producing novel theoretical statements
because it is possible to run the simulation across many different scenarios that
may have not emerged in the physical world. It may be possible that patterns
and regularities will emerge from a simulation study that have not been
observed or recognised as a significant form of behaviour in the physical world.
Thus, it is possible for simulation models to generate theoretical propositions
that may be subsequently tested in the physical world. The fact that simulation
methodology has not been widely used in strategic management to test existing
theoretical propositions or generate new theoretical propositions provides the
over-riding rationale for this study.
A middle way between prose and mathematics
The second major justification for simulation views it as as a middle way
between prose and rigorous mathematical modelling (Bruderer, 1993). It should
be noted that prose has long been the preferred vehicle for the presentation of
theoretical ideas in strategic management. Despite relying heavily on economic
concepts, normative researchers in strategic management have shown a
reluctance to adopt the formal mathematical methods prevalent in mainstream
economics. Periodically, calls have been made to adopt more formal methods
in the field, particularly the use of game theory (Camerer, 1991; Saloner, 1991).
Critics are concerned that the field has placed an excessive emphasis on
induction over deduction as a method of scientific inquiry (Camerer, 1985).
1
the limitations of the simulation approch are described in Section 3.2
85
For example, Michael Porter has described his five-forces model as a
framework for organising management thinking (Porter, 1980; Porter, 1985).
Although based on decades of empirical research in industrial organisation
economics, the resultant model is very unclear about the causal links between
the five factors of the framework and sustainable competitive advantage.
However, Porter is able to draw on a large set of relevant cases to entertain
salient aspects of the theory.
This inductive approach flows through much of the research in strategic
management, including resource-based approaches. The close proximity of the
discipline to management practitioners has dictated that theories should be
accesible, easily interpretable and intuitively appealing to lay scholars.
However, as the field matures and gains increasing sophistication, researchers
are beginning to turn their attention to mechanism-based theories which
attempt to explain the precise causal process leading to competitive advantage
(Teece et al., 1990).
The use of formal methods, including mathematics and simulation, forces
the researcher “...to be precise about the relationship among entities, to make
implicit assumptions explicit, and to describe in detail the mechanisms by
which entities and relationships change” (Carley & Prietula, 1994, p. xiv). It is
only after this operationalisation of key concepts has occurred that detailed
theory testing can commence.
As a tool, simulation has some inherent advantages over mathematical
methods. The greatest advantage is the ability of simulation to capture more
aspects of the real world (Cyert, 1994). While mathematicians “... have no
principled objection to dynamic models, to theories with more than two actors,
or to specifications of complex sets of goals...it is the demand of analytical
tractability that is constraining” (Bendor & Moe, 1992, p.124).
One of the key factors underlying the resistance to game theoretic
approaches in strategy is the required tradeoff between rigour and relevance
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(Oster, 1990). Even quite simple games do not have a closed form solution. It
is clear that mathematical models will continue to struggle to capture the
complexity of the task when the central problems of corporate strategy involve
firms competing in dynamic environments with many actual or potential
competitors. One approach is to make the simplifying assumptions needed to
generate closed form solutions and then attempt to generalise from the resultant
rigorous conclusions. The approach in this piece of research is to relax the
requirement for rigour in order to admit a wider range of relevant factors into
the analysis.
Types of Simulation
Simulation is really an umbrella term that covers a wide range of
approaches and traditions. We argue that simulations can be classified
according to the purpose of the simulation and the approach adopted to
modelling the entities in the model. These distinctions are used to highlight the
approach adopted in the current study.
Distinguished by Purpose
The purpose for choosing to simulate a system can be driven by either
confirmatory or exploratory considerations. Confirmatory simulation “...aims at
understanding the operating characteristics of a total system when the behavior of
the component parts is known with a high degree of accuracy” (Cohen & Cyert,
1961, p. 317). The usual purpose of confirmatory simulation is to combine the
system components to achieve a desired or optimal state. Thus management
scientists might use simulation to determine the optimal arrangement of machines
in a factory in order to maximise efficiency and performance. What is important to
note is that the inputs, outputs and running costs of each machine in the factory are
already well-specified in advance.
Simulation games are examples of confirmatory simulation. In such
games (whether behavioural or computer-mediated) the rules of the game are
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clearly set out in advance. Players attempt to achieve some pre-specified goal
within the artificial constraints of the game. Learning and knowledge transfer
occurs because the simulation can successfully mimic salient aspects of reality;
the act of playing the game creates the learning experience (Dutton & Stumpf,
1991).
Exploratory simulation aims “...to derive a set of component relations
that will lead to a total system exhibiting the observed characteristics of
behavior.” (Cohen & Cyert, 1961, p. 317). It is often the case in the social
sciences, that the outputs of a complex system can be easily measured and
observed. For example, the level of unemployment is a common economic
indicator collected by most governments of the world. What is not well known
is the underlying causes of unemployment.
In exploratory simulation, the researcher constructs a model of the
relations between the elements in the system to be simulated. According to
philosophers of science, good scientific theories must be falsifiable and remain
unfalsified (Chalmers, 1982). For example, the statement “it will not rain
today” is falsifiable. If it is observed that rain does fall during the day then the
theory would be falsified. A simulation model that can produce output that
mimics a real-world system therefore represents a satisfactory theory of the
phenomenon in question. In effect, the researcher is theorising that „this
simulation model reflects the underlying mechanisms in the real-world system‟.
If the output of the real-world system were to differ from the output of the
simulation model then the theoretical statement implicit in the simulation
model would be falsified. Thus, the goal of exploratory simulation is to
produce unfalsified and therefore plausible theories of reality.
Exploratory and confirmatory simulations clearly have two distinct
purposes. A confirmatory simulation is searching for a particular constellation
of known components that will achieve some target level of output. The
designer of an exploratory simulation, having observed a pattern of output from
a system of interest, attempts to hypothesise a set of elements and relationships
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that can successfully reproduce the given output (at a given level of
abstraction).
However, it is important to note that the distinction between the
exploratory and confirmatory approach to simulation is not absolute. An initial
exploratory modelling of a system may lead to an acceptable match with the
real world system over a representative set of inputs. This effective validation
of the simulation model could then allow the researcher to perform
confirmatory tasks such as prediction and optimisation. Similarly, a lack of fit
(or rising expectations) in the confirmatory model may cause another
exploratory phase to begin. This ability to construct a dialectic process has been
identified by previous researchers (Morecroft, 1985).
The current study adopts an exploratory simulation approach. If anything,
the large number of theories presented in Chapter 2 serve to illustrate how little
we know about the mechanisms that generate sustainable competitive
advantage. Undoubtedly, each theory captures some kernel of truth, but the
saliency of a given theory as an explanation for a particular strategic event is
unknown. What is well-known is that the profitability, or rent-producing
capacity, of firms varies by market, location and time. The existence of firm
heterogeneity is one of the fundamental premises in the strategic management
literature (Schoemaker, 1990). Arguably, the central research agenda in
strategic management is the search for explanations for this observed
heterogeneity.
The mechanisms underlying the observations of firm heterogeneity are
not well understood. As we have seen, there are a large number of competing
explanations for firm heterogeneity (e.g. industry forces, resource-based view,
organisational ecology), with some attempts being made to construct integrated
theories of strategy (Amit & Schoemaker, 1993; Barney, 1986b; Montgomery,
1995). To date, the relative contributions of competing explanations are only
weakly understood (Rumelt, 1991). Exploratory simulation enables an
interpretation or recombination of past theoretical efforts into a dynamic
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synthesis. This synthesis, in turn, acts as a conjectural statement about the
possible mechanisms underlying competitive advantage. New insights, or an
ability to replicate observed behaviour, are the rewards of these endeavours.
On the other hand, a decision to focus on confirmatory simulation would
be making the rather extraordinary claim that the components and relations
underlying competitive advantage are well understood and need only be applied
to the problem at hand in order to generate a solution. Imagine a system of
rules, with each rule prescribing a strategic action for the firm given some state
of the world. Would any strategy theorist be bold enough to suggest that they
could construct such a set of rules to guarantee firm profitability? We think not.
The discipline has not yet evolved to a stage where it can confidently
recommend or predict the specific strategic actions that a particular firm or
group of firms should undertake at a given point in time. More work is required
to develop mechanism-based theories of strategy. Exploratory simulation has a
potentially important role to play in this process.
Distinguished by Approach
Macro-simulations are designed to emulate the input/output behaviour of
the system of interest by applying differential or difference equations to model
the system at a high level of aggregation (Sloman, 1981; Stanislaw, 1986;
Widman & Loparo, 1989). Examples of this approach are most commonly
encountered in physics, engineering, economics and biology.
For example, predator-prey simulations, such as the Lotka-Volterra
Model (Volterra, 1926), are able to emulate the proportions of predators and
prey found in an empirically-studied ecosystem without reference to the
individual behaviours of the animals such as fighing, mating and eating
(Drogoul & Ferber, 1994). In economics, simulations are able to reproduce the
historical flows of resources in an economy without needing to develop a
model of the individual buying decisions of households and firms within that
economy (Nelson & Winter, 1982).
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System dynamics, recently repopularised by Senge, represents another
well-known aggregative simulation methodology (Hall, 1976; Morecroft, 1985;
Senge, 1990). System dynamicists conceive complex systems as as a set of
flows and stocks. Flows into a stock represent an accumulation function while
outgoing flows create a decline in stock levels. For instance, the manufacturing
process can be seen as a flow of produced goods from one stock (raw materials
inventory) to another stock (finished goods inventory). The cumulative effect
of negative and positive flows, often with feedback loops, is believed to be a
major factor in the generation of complex system behaviour.
Critics of macro-simulation have described it as a „black box‟ approach
because it does not seek to explain the mechanisms through which inputs are
converted to outputs (Carley & Prietula, 1994). When Newton wrote in
Principia Mathematica “I do not pretend to explain gravity”, he was indicating
that his famous model had little to say about the mechanisms that caused
gravity to act on distant bodies.
Micro-simulations model the idiosyncratic behaviour of individual actors
or agents within the system thus creating a link between individual and
aggregative phenomena. Macro-level results are said to “emerge” from the
interplay of agents within the system (Waldrop, 1992). The terms „complex
adaptive system‟ and „distributed artificial intelligence‟ are part of the recent
terminology coined to refer to systems adopting the philosophy of
disaggregated simulation (Doran & Gilbert, 1994; Holland & Miller, 1991).
While a few successful examples of micro-simulation surfaced in the sixties
and seventies, it has been the availability of powerful object-oriented and
artificial intelligence progamming tools on the ubiquitous microcomputer that
has been responsible for the renewed popularity in the approach, particularly in
the social sciences (Carley & Prietula, 1994).
Aggregated models have served the physical sciences well; allowing
scientists to gain control over their environment by understanding regularities
in nature and avoiding the problem of “not seeing the forest for the trees”. The
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data requirements for aggregate models can be many orders of magnitude less
than
micro-simulations.
In operationalising disaggregated simulations,
researchers are often required to speculate about possible behavioural
mechanisms in order to generate the rich complexity required in their models,
whereas aggregated simulations can mimic aggregate behaviour without having
to speculate about micro-processes.
Not surprisingly, advocates of micro-simulation have been critical of the
aggregated approach to simulation (Drogoul & Ferber, 1994). They see the
retreat to aggregation as ensuring that macro-simulation models will ultimately
fail to provide a satisfactory explanation for effects arising from the purposedriven behaviour of individuals within the system (Waldrop, 1992). Macrosimulation models offer no obvious link from micro to macro phenomenon, so
attempting to map the complex parameters found in these models on to some
aspect of reality is not readily achievable. It is also natural to see the actions of
sentient actors as flowing from evaluative decisions on the part of those actors;
that the macro-level outcomes represent some accumulation of the actions of
individual agents in the system.
Macro-simulation
models
are typically constructed by defining
mathematical relationships among variables or by identifying empirical
regularities that can be fitted to a mathematical equation. For example, given
two actions A and B, a aggregated model might determine from empirical data
that action A is taken 40% of the time and action B is taken 60% of the time.
An aggregated model could then randomly assign action A with a probability of
0.40 to each member of the population. In micro-simulation models,
aggregation occurs as a bottom-up process, with the simulation focused on
modelling the behaviour of each member in the population. Disaggregated
simulations are increasingly using artificial intelligence to model individual
entities in the system.
Rothenberg provides a succinct summary of the modus operandi of the
macro approach:
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“Analytic [macro] simulations are indispensable in many situations particularly
when dealing with complex physical phenomenon involving vast numbers of
relatively small and relatively similar entities whose aggregate interactions obey
the „law of large numbers‟ ” (Rothenberg, 1989, p. 83)
On reflection, two fundamental principles (or assumptions) of normative
strategy research, that we wished to incorporate in our simulation models,
resisted the successful application of an aggregated approach to simulation in
the current study.
The first principle was that of firm heterogeneity, or the view that
differences in endowments between firms result in heterogeneous performance
outcomes. As Rothenberg points out, macro-simulations attempt to develop
aggregate laws of behaviour. These laws are most successful when the
individual entities are relatively similar. An assumption that the objects of
study (in this case individual firms) are fundamentally dissimilar makes the use
of a aggregated approach to simulation somewhat difficult.
The second principle militating against a macro approach was the
assumption that the firm is a goal-driven (or telelogical) entity. If firms are not
meeting their goals, management will attempt change or adapt the firm‟s
2
behaviour in order to do so . Given variations in the initial endowment of
resources, each firm‟s adaptations or goal-driven behaviour can diverge widely.
The ability to model the adaptiveness of individual firms is severely restricted
in aggregated simulations that instead seek to aggregate behaviours into lawlike generalisations.
An early reading of the „garbage can‟ simulation (Cohen et al., 1972)
provided an example of an alternative approach to simulation beyond
aggregated models. This study made an attempt to model heterogeneous choice
situations; with each choice situation attracting different combinations of
problems, solutions and actors. Furthermore, problems, solutions and actors
were able to shift and change between choice situations, making it almost
2
Although this is disputed by many organisational ecologists and some strategists who
see inertia as the natural state of affairs (Hannan & Freeman, 1984; Rumelt, 1995)
93
impossible to predict whether any particular choice situation would reach
resolution. Resolution became a function of the state of the entire model and
the various complex interactions between individual entities within the model.
Thus, the simple-rule driven behaviour of heterogenous agents was causing
higher-level complexity in the model without the modeller having to specify
aggregate functions. Further reading in the garbage can tradition, particularly
Masuch and La Potin (1989), illustrated the efficacy of artificial adaptive
agents in organisational research.
Confirmation of the existence and emerging importance of disaggregated
simulation using artificial adaptive agents was found in the work of the Santa
Fe Institute (Waldrop, 1992). Researchers at the Santa Fe Institute had been
working within a „complex adaptive systems‟ paradigm. One of the central
features of this view was that higher-order patterns of behaviour in complex
systems „emerged‟ from the interactions between adaptive agents. The agents
themselves might be only following simple rules but a large number of agents
interacting created complexity.
Biologists have been particularly interested in the approach because
relatively complex patterns of animal behaviour can be explained by the
paradigm. For example, the ability of a flock of birds to turn together and avoid
running into objects, was found to emerge when a group of simulated birds was
each given a small number of local rules to follow. No knowledge of the global
behaviour of the flock was required to produce the aggregate flock behaviour
(Waldrop, 1992).
The „complex adaptive systems‟ hypothesis has been applied to a wide
range of disciplines including economics, biology, physics, chemistry,
anthropology and geology. Rather than trying to impose homogeneity on agents
in their models, researchers at the Santa Fe Institute have embraced diversity of
action. Typically, in advanced simulations, agents have been modelled using
artificial intelligence algorithms that enable idiosyncratic evolution and
adaptation. No two agents needed to possess the same resources or action sets.
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In fact, in biological models, complex ecosystems were observed to develop,
with niche competitors and symbiotic relationships emerging (Levy, 1992).
Goal driven behaviour and heterogeneous endowments, the two major
obstacles to simulating strategic behaviour with aggregated tools, are a feature
rather than a limitation of the artificial agents approach to complex systems
3
research . Surprisingly, artificial agents have been used only once before in
strategy research (Bruderer & Mitchell, 1992). We attribute this to the relative
novelty of the approach (circa 1985) and the relative lack of programming
skills among faculty members in business schools (with a concomitant lack of
technical assistance, software and hardware). This study represents an attempt
to further develop the complex adaptive systems paradigm within strategy.
Limitations of Simulation
Until quite recently, the primary limitation of simulation was thought to
be validation (eg. Naylor, 1971; Stanislaw, 1986). The concept of validation
was also particularly rigid: “The only true test of a simulation is to measure its
predictive capacity against the real world” (Naylor, 1971, p.13).
Simulation models which failed to meet this rigorous test of validity may
have failed for several reasons. Stanislaw (1986) has argued that simulation
modelling proceeds in three stages:
• The researcher develops a theory of the target behaviour
• The theory is converted to a (mathematical) model
• The model is converted to a computer program
At each stage, the researcher is confronted with the task of validating the
resultant product. The program may be an invalid representation of the
3
Note that artificial adaptive agents are a tool or methodology for research into
complex adaptive systems rather than an extant theory in their own right. Strategy as a complex
adaptive system will be considered in the next chapter.
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simulation model. The simulation model, in turn, may have failed to capture
4
the nuances of its theoretical substructure . In this sense, failing to validate a
simulation program may not lead to an automatic rejection of the underlying
theory, although there may validity issues here too.
This tough line on validity represents a tradition of confirmatory
approaches to simulation, particularly in operations research and management
science. Clearly, computer-generated experts must be able to reproduce the
same results as human experts in a given situation. Similarly, system dynamic
simulations of factory output should generate the same output with a given
level of inputs (and machine layout) as a physical system. Without adequate
validation, such systems would be useless for their task of generating optimal
solutions to routine problems. The end-user needs to believe that the output can
be trusted, and to this end validation processes are extremely important.
Drawing on Saloner‟s (1991) arguments about the validity of game
theory (see Section 2.5.4), we would argue that exploratory simulations do not
need to possess a one-on-one correspondence with the real world. It is
sufficient that exploratory simulations are able to generate counter-intuitive or
surprising results (Masuch, 1990). The prisoner‟s dilemma result in game
theory was found to be surprising and has generated a great deal of research on
cooperative behaviour (Axelrod, 1984). In the same way, Cohen, March and
Olsen‟s (1972) garbage can simulation of decision making has spawned over
two decades of research (Masuch & LaPotin, 1989; Seror, 1994). The
philosopher of science, Imre Lakatos, characterised good science as progressive
science (Lakatos & Musgrave, 1970). A progressive research program needs an
on-going ability to generate new insights and novel findings. By this criterion,
both game theory and exploratory simulation could be labelled as progressive
research methodologies.
Versimillitude, or detail for details sake, is seen as a major problem in
exploratory simulation (Carley & Prietula, 1994; Cohen et al., 1995).
4
In some simulations the mathematical model is also an implicit theory of the process.
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Sophisticated simulation modelling tools now enable researchers to construct
very complex models. There is a temptation to keep adding plausible
relationships in the quest for additional realism. Also, by increasing
complexity, the researcher also increases the chance of finding a counterintuitive or surprising result. The downside is that the model can become as
hard as the real-world to analyse. Too much complexity creates tangled causal
relationships and an inability to control all parameters that influence the final
result.
Does this line of argument imply that we do not need to test the validity
of exploratory simulation models? The answer is no, validation is still
important. The difference is that exploratory simulations are focused on theory
generation and development. Structural representations of the phenomena may
be quite abstract, with the researcher interested in making general statements
about model outcomes, rather than specific predictions about optimal inputs
and outputs. Thus, exploratory simulations often represent only a partial theory
of the phenomena of interest.
Validation checks must still be performed to ensure that the model
operationalises the underlying theory in an appropriate way, and that the
program is an accurate representation of the research model. However, the
simulation output may only be capable of a general approximation to reality,
rather than possessing the ability to mimic specific real world results.
Attempting to find direct correspondence with simulated and real-world results
would not be appropriate.
For example, the strength of Cohen March and Olsen‟s (1972) garbage
can simulation of decision-making is not based on its ability to predict realworld decisions but rather on its ability to demonstrate that decision-making
and problem resolution can still occur in organised anarchies - a novel and
counter-intuitive finding. Similarly, Axelrod‟s (Axelrod, 1984) use of
simulation to discover robust strategies, such as „tit-for-tat‟, in repeated
prisoner dilemma games has informed real world practice despite Axelrod‟s
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game having only a passing acquaintance with any particular real world
situation.
Admittedly, it is not entirely clear that any hard and fast rules exist for
determining the validity of an exploratory simulation in the absence of output
validation. In many cases, it may be the plausibiliity and transparency of the
model itself that leads consumers of the research to trust the simulation‟s
5
conclusions . It is interesting that the on-going research program in garbage can
models has progressively introduced new (more plausible?) assumptions into
Cohen, March and Olsen‟s original formulation (March & Weissinger-Baylon,
1986; Masuch & LaPotin, 1989). The ability to alter assumptions and test the
robustness of conclusions may represent a strength rather than a weakness of
the exploratory approach.
5
determining “plausibility” may be just as problematic as determining validity
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A Learning Classifier System
To err is human, to really foul things up requires a computer
In Chapter 2, we described strategy as a deliberate search for rents on a
fitness landscape - a landscape that changed over time in response to
competitive and exogenous pressures. In the previous chapter, we argued that
an exploratory and disaggregated computer simulation was a valid, if
underutilised, method for studying such a complex system.
One of the central challenges in developing a disaggregated or microsimulation is the design of artificial agents to populate the simulated
environment. Although early approaches relied on random adaptation and
simple feedback loops (Cyert & March, 1963; Lant & Mezias, 1990), more
recent approaches have used artificial intelligence techniques to create
„intelligent‟ agents that learn about their environment and adapt to it in
purposive ways (Masuch & LaPotin, 1989). This chapter describes an
algorithm, known as a learning classifier system, which has been used with
some success to guide the actions of intelligent artificial agents on fitness
landscapes. The algorithm forms the basis for intelligent adaptation by artificial
agents in the current study.
The chapter begins with a description of a learning classifier system and
concludes with a simple application that demonstrates the ability of agents
within the system to seek higher levels of fitness on a simple fitness landscape.
The chapter that follows (Chapter 5) develops a more sophisticated model of a
strategic landscape based on the theoretical principles discussed in Chapter 2.
Introduction
A learning classifier system is “an artificial system that learns rules,
called classifiers, to guide its interaction in some specified environment”
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(Goldberg, 1983, 112). Any learning classifier system can be divided into three
distinct sub-systems (see also Goldberg 1983, 1989):
1. Rule and Message Sub-System
2. Reward Sub-System
3. Rule Generation Sub-System
Both learning classifier systems and expert systems are examples of
production systems. Production systems are computational devices that use
rules as their only algorithmic mechanism. The basis for the classifier system‟s
rule and message sub-system arises from earlier research on expert systems and
subsequent deficiencies in that approach.
An expert system can be defined as “...a program that relies on a body of
knowledge to perform a somewhat difficult task performed by a human expert”
(Parsaye & Chignell, 1988) p.16). As we have discussed in Chapter 2, early
research in artificial intelligence relied on pruning search trees through the use
of general heuristics (Newell & Simon, 1972). However, research on cognition
has discovered that human experts use general problem-solving approaches
only when domain-specific knowledge has failed. Experts have been estimated
to hold between 50,000-100,000 facts about their field acquired over a period
of time of no less than 10 years (Egidi & Marris, 1992). These rules are
commonly expressed as production rules having the general form:
if <condition> then <action>
For example, a doctor may determine that if a patient has symptom A and
symptom B then disease X should be diagnosed. An expert system attempts to
distill these production rules into a knowledge base or production system, with
rules being elicited from human experts through the services of a knowledge
100
engineer (Jackson, 1990; Turban, 1988). Expert systems are thus characterised
by:
• a focus on a narrow task or field of knowledge;
• the storage of production rules in a knowledge base;
• the ability to explain their own actions and lines of reasoning.
A growing number of expert systems has now been developed in strategic
management (Borch & Hartvigsen, 1991; Mockler & Dologite, 1992;
Schumann, Gongla, Lee, & Sakamoto, 1989) and organisational theory (Baligh
et al., 1986; Blanning, 1991; Masuch, 1990). Suprisingly, in a large number of
these cases, the knowledge bases for these systems were constructed from
theoretical or textbook knowledge rather than from the knowledge of expert
practitioners. For instance, an expert system for designing organisations
(Baligh et al., 1986) was based on theoretical constructs from contingency
theory. Using information such as size, technology and task structure, the
expert system was able to design an organisational structure (in the process
resolving conflicts between competing contingencies). Similarly, strategic
management expert systems have relied on rich strategic theories, such as
Porter‟s five forces model or the Directional Policy Matrix, to derive a large set
of production rules.
When linked to a dynamic model, expert systems are able to provide
“intelligent” reactions to certain states that may arise in a simulation. For
example, nuclear scientists can simulate a meltdown in a nuclear power plant.
Meltdowns can occur under a wide variety of states and conditions, and an
expert system module may be used in situ to diagnose the problem and suggest
remedial action. Complex expert systems are able to produce counter-intuitive
propositions which may surprise or enlighten human experts. Where theoretical
propositions are used in lieu of human experts (which frequently occurs in
organisation and management models), such counter-intuitive propositions may
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serve to extend the theory or reveal deficiencies in existing theory (Masuch,
1990).
While the use of an expert system to represent domain knowledge as a set
of production rules may appear unproblematic, in practice, the method is
subject to several deficiencies that restrict the wider application of the
technique. Perhaps the most serious problem is the difficulty in acquiring
knowledge from human experts. First, there is an assumption that such experts
exist. The use of theoretical propositions, rather than the knowledge of
practicing managers, to construct expert systems in the organisation and
management literature would suggest that human experts are not readily
available. Secondly, experts may be unwilling or unable to codify their
knowledge as production rules. The sheer amount of time required to elicit
large numbers of rules represents an obvious barrier. However, fear of
technological obsolesence and professional pride are also factors that often
underlie a general reluctance to participate in knowledge elicitation.
The observation that knowledge changes over time reflects the second
major challenge to expert systems. The knowledge engineer essentially gathers
a snapshot of an expert‟s knowledge at a point in time. Humans, however, are
constantly updating their knowledge base through experience. New problems
will arise that require new rules. Old rules may have to be adjusted to reflect
the availability of new conditions or new remedies. An example of this is when
medical practitioners must change their knowledge base to incorporate a new
drug or treatment. Expert systems do not have this ability to automatically
change or update their knowledge base. Knowledge engineers must often
expend substantial resources simply maintaining a rulebase. The greater the
volume of new information and the more complex the domain, the greater the
effort required to maintain the system.
When they are working well, expert systems are quite capable of
mimicking the actions of human experts. Introductory textbooks refer to
numerous cases where expert systems have performed as well, if not better
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than, human experts (Jackson, 1990; Turban, 1988). Expert systems are faster
and more reliable than human experts; require no rest periods; and can be
copied and distributed at low cost.
Given the advantages of automated knowledge, it is no surprise that
computer scientists have attempted to bypass the limitations of expert systems;
limitations which primarily arise from the need to elicit and maintain
knowledge from human experts. Machine learning is the term used in
computer science to describe the acquisition of knowledge without a human
expert.
A learning classifier system is a form of machine learning which
dispenses with a human expert and attempts to evolve a meaningful rulebase
via environmental feedback and the recombination of existing rules to form
new improved rules. Like an expert system, all knowledge in a learning
classifier system is coded as production (if...then) rules. A learning classifier
system is thus an expert system - without an expert.
The representation of knowledge as production rules has several
advantages for strategy researchers (Bruderer, 1993). First, the <if...then> form
of a production rule accords well with the concept of strategy as an action or set
of actions contingent on the environment (Child, 1972). As we have discussed
in Chapter 2, environmental analysis (embodied as SWOT) forms one of the
central tenets of strategic planning.
Secondly, production rules are directly examinable by the researcher. At
the conclusion of a simulation run it is possible to study the rulebases of each
actor in the simulation. Studying the production rules of successful and
unsuccessful players in a simulation may reveal novel or surprising strategies
(the occurrence of which we have defined as the raison d’etre of exploratory
simulation). Axelrod (1987) used machine learning techniques to evolve novel
strategies for the prisoner‟s dilemma game. He was surprised when several of
these strategies outperformed his classic „tit-for-tat‟ strategy - previously
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thought to be the dominant strategy. The transparency of the machine learning
approach enabled him to investigate the exact reasons why those strategies
were successful.
Thirdly, in a learning classifier system, production rules will evolve
independently for each agent thus allowing for the possibility of heterogeneous
actions (and hence outcomes) in response to a given stimulus. We have earlier
argued that techniques used to model corporate strategy should have the ability
to reproduce the observed heterogeneity of performance among firms in the real
world. Learning classifier systems possess this capability.
When the knowledge base of a learning classifier system is initialised, it
contains a set of rules with random associations between conditions and
actions. The algorithm relies on two methods to improve the quality of these
rules in its knowledge base. The first method involves rewarding rules for
successful performance in the external environment. The process of rewarding
rules is complicated by the fact that a chain of rules may be involved in
reaching a desirous external state. Credit for reaching an external state must
thus be apportioned among participating rules. Over time, competition will
force successful rules to emerge from the rulebase as they receive more credit
and their average fitness (or strength) increases. Similarly, the strength of poor
rules will fall relative to the average rule in the knowledge base.
Over time, the reward sub-system will discriminate between „good‟ and
„bad‟ rules in a given rulebase vis-a-vis the task environment. However, it must
be remembered that these rules will only form a small sub-set of all possible
rules. A second mechanism is required to replace poorly performing rules with
potentially better rules. Ideally, this search for an optimal set of rules should
not involve a blind or random search of the entire set of rules. The assumption
underlying intelligent search in the reproduction sub-system is simply that
better rules can be found by recombining already successful rules into new
sequences. As a direct analogue of sexual reproduction in biology, the search
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strategy draws its face validity from the complexity and diversity of survival
solutions found in the biological world.
A learning classifier system thus uses the biological principles of natural
selection (survival of the fittest rule) and sexual reproduction to evolve its
rulebase towards higher and higher levels of performance. In the next sections
we examine the operation of the three sub-systems in more detail.
The Rule and Message Sub-System
The characterisation of information processing systems as manipulators
of symbols has formed the basic premise for an influential line of research in
the field of artificial intelligence (Gazendam, 1992; Newell & Simon, 1972).
Much of the previous simulation work in the organisation and management
literature has also explicitly (or implicitly) adopted this design philosophy
(Carley & Prietula, 1994; Cohen et al., 1972; Masuch & LaPotin, 1989).
A physical symbol system is any system that represents the state of
objects in the problem domain as a symbol or collection of symbols (termed a
symbol structure). For instance, the use of the symbol „1‟ might represent the
presence of a firm in an industry, while the symbol „0‟ might signify the
absence of a particular firm in the same industry. Collectively, the symbols are
referred to as an alphabet.
The set of all possible symbol structures in a domain is known as the
problem space. In the problem space, some states of the world, and hence some
symbol structures, may be preferred to others. Physical symbol systems also
need production rules, or operators, to create, modify or destroy symbol
structures (Newell & Simon, 1972).
The task of finding a preferred, or optimal, state can be extremely
difficult when the number of symbols or symbol structures is large. As the
number of objects in the symbol system is increased, the size of the problem
space increases exponentially. This combinatorial explosion creates search
105
costs, both in terms of time and resources. Most simulations use one or more
search strategies to evolve the symbol system towards a preferred state over
time. Thus, search involves generating and progressively modifying symbol
structures until a solution structure is found.
Expert systems and learning classifier systems are both examples of
physical symbol systems. Expert systems typically use literal symbols in their
production rules that have a direct correspondence with the phenomenon being
modelled. Thus, a production rule in a car repair system might follow the form:
if <engine is off> then <turn key>. Conversely, learning classifier systems
restrict their alphabet to only three symbols - 1, 0 and # - where # is understood
1
to be a wildcard (either a 1 or 0) . Thus a production rule in a learning classifier
system may be written as: 100011#1# -> 100011. A secondary mapping from
binary symbols to literal referents is required in order to interpret the
production rules in this form.
Several advantages arise from using a system of binary symbols and
wildcards even though they appear more clumsy than the literal representation
of knowledge found in expert systems (Holland, 1975). First, any real number
to a given level of precision can be approximated by binary digits. Secondly,
the use of binary symbols allows the use of genetic algorithms to construct new
rules from old rules (see 4.4 below). Finally, the use of the wildcard symbol
creates what Holland calls „implicit parallelism‟. By evaluating the fitness of a
wildcard condition #11 we are also implicitly testing (to some degree) the
fitness of conditions 011 and 111. Thus, the wildcard acts as a schema to
quickly delineate relative regions of fitness.
1
Wildcards are only used in the condition part of the rule.
106
Figure 4.10 Conceptual model of a learning classifier system
The rulebase of a learning classifier system consists of a finite number of
production rules formed from the ternary alphabet. Metaphorically, the rulebase
is the “brain” of the system. As with a human body, the “brain” requires several
other mechanisms to interact with its environment (see Figure 4.1). Detectors
act as the senses of the system, translating environmental stimuli into binary
coded messages that the rulebase can interpret. Incoming messages are then
passed to a message list (which can been likened to short term memory).
Messages on the message list are matched with the conditional part of
rules in the rulebase. For example, given the rulebase:
1) 101->101
2) 001->111
3) 1#1->011
4) 1##->000
a message of 101 would match rules 1, 3 and 4 (# being a wildcard that
matches 1 or 0).
Arbitration must occur at this stage to determine which rule will be
allowed to post its action component back to the message list. Arbitration is
necessary because the system has been arbitarily restricted to taking one action
at a time. The arbitration process, which takes the form of an auction, is
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described in detail in the following section (4.3). The action component of the
rule that wins the auction is then passed, via the message list, to the effectors.
The role of the effectors is to translate the binary-coded action from the
rulebase into a literal action interpretable in the environment.
It should be noted that in alternative versions of the algorithm, multiple
actions are allowed to be posted back to the message list. In some of these
cases, not all actions are passed to the effectors. A distinction is made between
internal and external actions. Internal actions are treated like new messages to
the message list and used to generate further matches in the rulebase. In the
current study, the operation of the classifier system has been kept as simple as
possible. Hence, only one message from the environment is posted to the
message list per round, and only one action originates from the rulebase to the
effectors per round (Figure 4.2 depicts a flowchart of the process).
108
Figure 4.2 Flow Chart of Clas if er Operation
Figure 4.11 Flowchart of Classifier Operation
109
Selection
A learning classifier system is an evolutionary algorithm, so-called
because it uses biological principles to improve the quality of its rulebase.
Natural selection, or the survival of the fittest, is an intrinsic component of the
system‟s operation. Rule strength (S) is envoked as a measure of the fitness of a
rule. When the system is initialised, all rules are given the same strength value.
The reward sub-system acts to alter this rule strength over time to favour rules
which are more effective in the task environment.
Rule strength can be adjusted in a number of ways during each round
(Goldberg, 1983). In general, the strength of a rule at time t can be determined
from the following equation:
A detailed examination of the process of making payments, incurring
taxation and receiving receipts will provide an understanding of the ways in
which classifiers in the rulebase become rapidly differentiated on the basis of
strength.
Payments
Each round, an incoming message from the message list may match
several classifiers in the rulebase. We have earlier discussed the need for
arbitration to prevent multiple (and potentially contradictory) actions from
occurring. All matched classifiers take part in an auction where they bid for the
right to be activated. The value of the each bid is then deducted from each
rule‟s strength as a payment. The classifier with the highest effective bid is the
one allowed to activate the action component of its rule.
A classifier‟s bid is proportional to its strength and its specificity.
Specificity is related to the number of wildcard symbols in the condition
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component of the classifier. The fewer the wildcards, the higher the specificity.
Biasing the auction towards classifiers with high specificity enables the
establishment of a default hierarchy where general rules (i.e. those with many
wildcards) can be overidden by exceptions to the general rules (Bruderer,
1993). This increases the robustness of the system.
The variable, matchscore, is used to measure specificity and is simply a
count of the number of non-wildcard symbols in a bidding classifier‟s
condition component:
Once the value of matchscore has been determined, the bid value for each
classifier can be determined from the following equation:
This bid value is the payment, P, that is deducted from the strength of
each bidding rule. The process of effectively punishing rules for participating in
the bidding process enables winning rules (and particularly highly specific
rules) to be rapidly differentiated from less well-matched rules. General rules
can still do well if they match a wide variety of conditions for which no
specific rule performs well thus establishing the default hierarchy principle
discussed earlier.
The winner of the auction is almost never selected on the basis of the
highest bid alone. It is possible to envision a case where a moderately
successful but highly specific rule wins the bidding in an early round against a
less specific but ultimately more successful rule. The less fit rule increases its
strength through good performance, and then continues to win auctions because
of its higher strength even though a less specific but more rewarding rule
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exists. To guard against this situation, some random noise is added to the bid
value to form an effective bid:
The winner of the auction is the classifier with the highest effective bid.
Taxation
Goldberg (1983, 1989) claims that taxation provides the opportunity to
bias the rulebase towards more productive rules. Taxation is levied on all
classifiers in the rulebase proportional to strength:
In theory, the average strength of classifiers in the rulebase will fall over
time thus causing successful rules to stand out from the crowd (because of the
level of rewards received). In practice, in a large rulebase where even
successful rules might only win auctions infrequently, taxation has the effect of
masking or submerging success. We therefore advocate either very low levels
of taxation (around 0.1%) or none at all.
Receipts
Classifiers can receive receipts from two sources. The first source of
reinforcement is from the environment. If the action of a given classifier results
in a performance-enhancing outcome for the agent being modeled then the
strength of the classifier is adjusted in proportion to the outcome. In the case of
a firm, the ability for a classifier to increase net profit may form the basis for
reinforcement. Various schemes exist for determining the level of reward. The
reward may be based on a fraction of absolute performance; the relative change
in performance; or a combination of the two.
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Actions which result in performance degradation are punished with a
decrease in classifier strength. Consideration must be given to the nature of the
punishment, and this can only occur in the context of the behaviour being
modeled (i.e. the punishment should fit the crime). In a business context, rules
which produce a net loss or lower profit for a firm are clearly undesirable. Loss
making actions should attract more severe penalties than the rewards that
accrue to profit-making actions. Reducing the strength of a loss-making rule to
zero is one way of ensuring that the classifier would never be selected again.
A second form of receipt occurs when a desire to reinforce a chain of
actions, rather than a single classifier, exists. The game of chess is a good
example. In chess, the winning move only occurs after a long chain of other
moves. If a chain of moves results in performance-enhancing behaviour then
previous classifiers in the chain should also be rewarded. The length of the
chain and the nature and level of reward to each member of the chain are some
of the issues that must be resolved in implementing what Holland has called the
“bucket brigade” algorithm (Holland, Holyoak, Nisbett, & Thagard, 1986) and
others have called “profit sharing” (Bruderer, 1993). For instance, how does
one reward a loss-making activity that leads to a superior outcome later in the
game (such as sacrificing a pawn or queen to win a game of chess)? We have
minimised the role of profit-sharing in our own simulations because of these
difficulties. Future studies may choose to explore these aspects of classifier
systems.
Creation of New Rules
After several periods of operation, the reward sub-system will have
effectively differentiated among the classifiers in the rulebase on the basis of
strength. Strong classifiers will be those that increased the performance of the
agent in its given environment. Classifiers that failed to perform will have a
strength approaching zero.
113
In a complex system, the classifiers in the rulebase will represent only a
fraction of the possible symbol structures in the problem space. It is thus highly
probable, particularly at the start of a simulation, that the rulebase will not
contain the best rules. The task of the rule generation sub-system is to replace
weaker rules with potentially better rules. The only information the system has
to guide its search strategy is the differential strengths of each rule in the
rulebase (and their associated symbol structures). A technique known as a
genetic algorithm is used to generate the new rules. Genetic algorithms use
methods analogous to the biological processes of sexual reproduction and
mutation to modify symbol structures (Goldberg, 1989).
Periodically, after allowing the reward sub-system to operate for an
arbitrarily long period of time to achieve good rule differentiation, the genetic
algorithm is applied. The first step is to rank the N classifiers in the rulebase
according to strength. The bottom N/2 rules are discarded to make way for new
rules. In this way, only the fitter, stronger and more successful rules are
retained in the rule population and allowed to reproduce.
114
ess Landscape
5 4 3 2 1
Figure 4.12 Principle of roulette wheel selection (from Fogel, 1995)
0
The N/2 weaker rules are replaced in a process akin to sexual
reproduction in the biological world. For each new rule, a “mother” and
“father” are chosen from the N/2 surviving rules (Bruderer, 1993). Several
methods are available to guide the choice of parent classifiers. Goldberg (1983,
5
115
10
20
1989) favours a roulette-wheel selection process where the probability of
selection is proportional to the rule strength (see Figure 4.3). This has the effect
of biasing search towards the regions populated by the most successful rules.
Bruderer (1993) argues that the search should be more diversified and
advocates giving each surviving rule an equal probability of being selected as a
parent (a procedure he calls „love-blind‟). Our models use Goldberg‟s roulette
wheel selection unless otherwise stated.
After selecting two parent rules, the “child” rule is typically constructed
by choosing a random crossover point along each of the condition and action
components of the parent rules; the child rule consisting of the father‟s symbol
structure to the left of the crossover point and the mother‟s symbol structure to
the right of the crossover point (see Figure 4.4). The strength of the new rule is
determined by taking a weighted average of the parents‟ strengths:
116
ess Landscape
5 4 3 2 1
Figure 4.13 The crossover process
0
As in nature, the presence of mutation allows a population to generate
novel combinations not found in the parents‟ gene pool. This enables the
genetic algorithm to explore regions of the search space beyond the set of all
possible rule recombinations in the rulebase. For each new symbol created in
5
117
10
20
the child rule there is a very small probability that a “mutation” will occur. In
this case, the symbol is randomly determined as 0, 1 or wildcard in the
condition component and 0 or 1 in the action component. The presence of
mutation guarantees that the system will eventually find an optimal solution
regardless of starting position although this may take an extremely long time
depending on the gap between initial and final positions (Fogel, 1995).
Premature convergence occurs when a sub-optimal rulebase is created
that requires simultaneous multiple mutations to reach a state of higher fitness.
As the probability of multiple mutations occurring is very low, the process is
practically ended. Bruderer sees his „love-blind‟ mating as a way of potentially
avoiding premature convergence. Several other schemes have also been
suggested (see also Fogel, 1995).
Crowding
Replacing the worst performers in a population with „children‟ of the best
performers can rapidly deplete the diversity of classifiers in the rulebase
(Goldberg, 1983). This has the potential to lead to premature convergence
because potential solution spaces are discarded too early. Instead of
sequentially replacing the worst performers, a sample of M low performing
classifiers is taken. The crowding routine replaces the classifier from the
sample that is most similar to the „child‟ classifier created by two high
performing parents. The degee of similarity is simply a count of the number of
times a symbol appears in the same position for both the child and low
performer (the strings 100#11 and 100010 would have a similarity count of 4).
The effect of this operation is to slow the rate of loss of diversity.
The size of the sample of low performers has a significant effect on the
operation of the crowding routine. Large samples are more likely to replace the
same rule over and over again leading to a slower loss of diverse rules.
However, effectively slowing down the replacment of poor performers slows
down the rate of convergence to a solution. Large samples also increase the
118
amount of computation required and hence further slow down the process.
Previous work seems to have favoured relatively small samples of around 9-10
rules (Goldberg 1983).
Scaling
Two problems arise when the strength of a small number of successful
classifiers becomes large relative to the rest of the classifiers in the rulebase.
First, the weighted roulette wheel becomes heavily dominated by strong rules
leading to a loss of diversity in the operation of the genetic algorithm and
increasing the possibility of premature convergence. Secondly, combining
weak and strong rules tends to develop rules with moderate strength (as the
strength of a „child‟ rule is the weighted average of the parents‟ strength).
Moderate strength rules will have little chance of winning auctions against
strong rules thus perpetuating the status quo.
One solution to this problem is to rescale the rule strengths (Goldberg,
1989). Applying a logarithmic transformation to the rule strengths before the
operation of the genetic algorithm is a typical way of reducing the range of
strengths in the rulebase. There is an underlying assumption in this technique
that although strong rules are good rules they are not necessarily the best rules.
Thus, the technique promotes diversity (and hence a greater search of the
problem space) at the expense of stability. Good rules become easier to change
or discard. The problem is that the “best” rules also become easier to change or
discard. This can make the performance of the system erratic as good rules
enter and exit the system.
Application to a simple strategic problem
In this section, we introduce a simple strategic game in which two players
strive to increase their profit performance vis-a-vis each other. The payout
figures in each round are jointly determined by the actions of each player and
profit represents the accumulation of payouts from each round. Each player is
119
represented by an artificial adaptive agent that uses one of four adaptive
strategies, including a learning classifier system, to improve performance
throughout the game. The results of a round robin tournament are reported at
the end of the section. The tournament enabled us to test the efficacy of each
adaptive strategy against the other three.
The objective of the game is to maximise profit or payouts by selecting a
combination of three binary digits (e.g. 101, 111, 000). Having the first binary
digit in the „ON‟ or „1‟ position is a necessary but insufficient condition to
generate a positive payout. The last two digits in the binary string determine the
share of profits. Given the first digit is „1‟ for both players then the profit
payoff is a function of the share of resources in the second and third positions
less a resource charge:
Thus if player A‟s strategy is the binary string 101 and player B‟s strategy
the string 111, then player A would receive a payoff of
and Player B would
receive a payoff of . If both played the string 111 then the payoff would be for
each player. Table 4.1 displays the complete payoff matrix for the game.
Note that the value of the last two digits in the binary string are summed in the
matrix. Thus the strings 110 and 101 both have a sum of 1 for the last two
digits (being functionally identical in the game). In total, there are
combinations of binary strings between the two players.
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possible
Table 4.4 Payoffs for a simple strategic game
Value of first
binary digit
A
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Sum of last
two digits
B
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
A
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
Payoff
B
0
0
0
1
1
1
2
2
2
0
0
0
1
1
1
2
2
2
0
0
0
1
1
1
2
2
2
0
0
0
1
1
1
2
2
2
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A
0.00
-0.10
-0.20
0.00
-0.10
-0.20
0.00
-0.10
-0.20
0.00
-0.10
-0.20
0.00
-0.10
-0.20
0.00
-0.10
-0.20
0.00
0.90
0.80
0.00
0.40
0.47
0.00
0.23
0.30
0.00
0.90
0.80
0.00
0.40
0.47
0.00
0.23
0.30
B
0.00
0.00
0.00
-0.10
-0.10
-0.10
-0.20
-0.20
-0.20
0.00
1.00
1.00
0.90
0.90
0.90
0.80
0.80
0.80
0.00
0.00
0.00
-0.10
-0.10
-0.10
-0.20
-0.20
-0.20
0.00
0.00
0.00
0.90
0.40
0.23
0.80
0.47
0.30
Game Strategy
If Player B chooses any string where the first binary digit is zero (i.e. 000,
001, 011 or more generally 0##) then the optimal course of action for Player A
is to choose a strategy with a single resource (either 101 or 110) to obtain a
payoff of 0.90. If both players contest the game by choosing „1‟ for the first
digit then the maximum payoff is 0.40 (each player choosing a „1‟ for only one
of the last two digits - 101/101, 110/101, 101/110 or 110/110). However, if
Player A believes that Player B is going to play the strategy 110 or 101 then
Player A could improve his or her result by choosing the strategy 111.
Moreover, if Player B played the strategy 111, Player A would find that the
string 111 still yielded the highest payoff. In game theory parlance, 111 is a
dominant strategy, that is, it is the best strategy regardless of what the other
player does (Dixit & Nalebuff, 1991).
Rational expectations theory would predict that players would play the
2
dominant strategy whenever the first digit of both players was „ON‟ . However,
in the past, human subjects have often diverged from theoretical optima while
playing similar types of games (Bruderer, 1993). It would be unreasonable to
expect artificial agents using human learning strategies to perform in different
(or better) ways to human subjects. Accordingly, we resolved to test a number
of human subjects on the same game.
Human Testing Study
An advertisement for pairs of subjects to participate in a “computer
simulation of corporate strategy” was posted on the School of Commerce
noticeboard in late October 1996. Announcements were also made in first year
management and second-year marketing classes (each containing over 200
students). Subjects were induced to compete in the study by a promise of up to
$9 for a one hour testing session and a $100 prize for the winner of the overall
2
technically, 111 is only a dominant strategy for the sub-game defined by both players
selecting „ON‟ or „1‟ as their first digit.
122
simulation game. A total of twenty pairs or forty subjects volunteered for the
study.
Interaction between players was controlled by a purpose-built program
run on Apple Macintosh computers. Each subject was located at a different
computer terminal and maximally separated from their opponent to prevent
collusion. In each round, a subject was required to enter a three digit binary
code. Each pair of decisions was processed simultaneously with the payoff
being determined as per Table 4.1. Subjects were instructed to attempt to
maximise their cumulative payoff (see Exhibit 4.1) Pairs of subjects played a
total of 100 rounds each and required around 30 minutes to complete the task.
At the end of the game, winning subjects were rewarded with $0.20 for each
full point of profit in the game (up to a maximum of $9). The highest scorer
overall was awarded a prize of $100.
The reward structure was intended to motivate participants to a) beat their
opponent, and b) continue to maximise profit even when their opponent was
beaten in order to win the overall prize. This extrinsic reward structure was
designed to mimic the reward system employed with our artificial agents
(which were rewarded on absolute profit levels and lacked the human quality of
mercy). In hindsight, the reward system for the human study provided too great
an incentive for unscrupulous pairs of subjects to collude.
Many of the participants behaved in a manner that suggested they had
made private arrangements prior to the start of the game (known as
sidepayments in the game theory literature). The effect of these arrangements
was to maximise the payoffs to one player at the expense of the other with
some deal to split the extrinsic reward of $100 after the game. The behaviour
was manifested from the very start of the game. An initial learning phase of
five to ten rounds, during which the participants learned to maximise the profit
of a single player, was followed by a stream of strategies where the designated
“loser” cycled through some of the worst strategies available (e.g. 000, 011,
001, 010). This resulted in very high payoffs to the other player.
123
Faced with the prospect of high payouts to winners and little meaningful
data, we altered the extrinsic reward structure to reward both the winner and
loser $0.10 per point of profit. It was hoped that this would motivate the losing
party to improve their score. However, the $100 incentive proved too great, and
collusion still continued to occur. Finally, we separated self-selected pairs of
players at the start of the game and reassigned them to unfamiliar partners. In
hindsight, this should have been done from the start, but a desire to gather a
large sample of participants led us to promote self-selection of pairings over
random assignment. Ironically, the collusive aspects of the study resulted in
reliable data from only seven cases.
Figure 4.14 Frequency of dominant strategy (n=7)
However, even this rather modest sample was able to confirm that human
subjects tended not to follow a dominant strategy even after sustained periods
of learning. Figure 4.5 depicts the percentage of cases in a round where both
players used the dominant strategy, 111. Predictably, there was a growth in the
percentage of players adopting the dominant strategy over time as players
learned the game but nevertheless the figure failed to reach unity even after 100
rounds (contrary to rational expectations).
Anecdotally, players appeared to be aware of the dominant strategy quite
early in the game. Those winning the game tended to stay with the 111 strategy
because it was the strategy of least regret. As long as the leader used 111, the
follower could never obtain a higher payoff than 0.30 (by also playing 111).
More importantly, the leader also received a payoff of 0.30 and thus could
never be overtaken. In every case, the early leader went on to win the game.
The frustrated followers were driven to try various strategies to improve their
performance but discovered that all other strategies (other than 111) resulted in
lower payoffs. Despite this realisation, many followers refused to submit to the
evidence and continued to cycle through various strategies for the remainder of
124
the game. This drawn out experimental phase accounted for the failure of both
players to consistently use the dominant strategy. If the game had continued for
another hundred rounds then presumably most losing players would have
accepted that 111 was also the strategy of least regret for them.
In one or two cases, players briefly discovered co-operative strategies that
shifted payoffs from 0.30 to 0.40 (e.g. 101/110). Given Axelrod‟s (1984) work
on the tendency for co-operation to develop in iterated Prisoner dilemma games
it was somewhat surprising that co-operation did not emerge more often, and
was so transitory and fragile when it did.
In conclusion, while the human testing study greatly underestimated the
inclination for business students to collude, it succeeded in demonstrating that
dominant strategies must be learned and even after being discovered are not
always followed. Winning players were more likely to use the dominant strategy
because it consolidated their positions. Losing players, on the other hand,
became more experimental in the hope of uncovering a hidden winning move.
125
Exhibit 4.1 Instructions to Human Subjects
LEARNING TO MANAGE A SIMULATED BUSINESS
IN A COMPETITIVE ENVIRONMENT
Introduction:
Welcome and thank you for agreeing to participate in this study. The purpose of the study is to
examine the ways in which people learn to manage a simulated business in a competitive
environment. Your goal is to make as much profit as possible in 100 decisions. As an incentive
to make as much profit as possible, the winner of the game will be paid $0.20 for each dollar of
game profit. At the end of the study, the highest profit earner will also earn a prize of $100.
Instructions:
The simulated business you are about to run has three attributes. You may like to think of these
attributes as price, quality and service. However, don‟t assume that this game operates like the
real world. A high quality does not necessarily mean you need high prices or high service. In
fact, the whole point of the study is for you to learn which combination of attributes produces
the best profit for your firm.
In each round, you will be asked to enter a three digit number. This number must contain either
zeros (0) or ones (1). For example,
101, 010, and 111
are all valid combinations, and
1001, 11, and 123
are not valid combinations.
A one represents a high level on a particular attribute, while zero represents a low level on a
particular attribute. Thus, the decision
110
would mean that the first and second attributes are both set at a high level, and that the third is
low.
After each decision, the results of your decision will be displayed. A sample set of results
appears below:
Round:5
Player One
Player Two
Strategy
101
110
Profit
0.23
0.47
Cum. Profit
1.23
1.45
To win the game, you must adjust your decisions to beat your opponent‟s cumulative profit.
The winner of the game will be paid $0.20 for each dollar of accumulated profit. To win the
final prize of $100 you must have the highest cumulative profit of all game winners. Good luck!
126
The Tournament
The use of a tournament to determine the best strategy for playing a game
was popularised by Robert Axelrod in his study of the iterated prisoner‟s
dilemma (Axelrod, 1984; Axelrod, 1987). At the commencement of the study,
Axelrod invited participants to specify algorithms for playing this wellspecified game-theoretic problem. He then pitted the algorithms against each
other in a round-robin tournament with the winning strategy being the
algorithm with the highest average payoff per game.
In the current study, four adaptive strategies were used to play a roundrobin tournament involving the simple strategic game outlined earlier. Each
individual game lasted 200 rounds. One hundred and twenty-eight games were
played for each of the sixteen combinations of strategies, resulting in a total of
2048 runs of the model. The code for the tournament can be found in Appendix
A.
The four strategies utilised were:
1. A Learning Classifier System.
2. A Classifier System
3. Random Walk
4. An Aspirational Feedback System
Learning Classifier System
This strategy utilised the principles of the learning classifier system
algorithm introduced in the early sections of the chapter. The rulebase of the
learning classifier consisted of 20 rules (from a possible 64). The condition
component of each rule was matched against the action of the competitor in the
previous round, while the action component represented the action to be taken in
the current round. A typical rule thus had the form:
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101->110
suggesting that if the competitor‟s previous action was the binary string 101
then the player should take the action 110.
All rules that matched the competitor‟s previous action were allowed to
3
bid for activation . The bid strength was a function of the rule strength plus a
random Gaussian error with mean of zero and standard deviation equal to a
quarter of the rule strength. Thus, at the start of the simulation, 95% of bids fell
between 1.5 and 4.5 units (given an initial rule strength of three units for each
rule). The single rule with the highest bid strength was allowed to activate the
action component of its rule.
After the payoffs for an action had been determined, the strength of the
winning rule was updated in line with performance. This reward (or punishment)
was a function of two factors: the absolute performance level; and the differential
performance between the two players:
The reward scheme had the effect of severely punishing rules that failed
to earn a positive payoff. Rules were also penalised if they failed to outperform
the competitor because obtaining a greater profit (or cumulative payoff) than
the competitor was determined to be the most important objective of the game.
Thus, a strong rule that produced positive payoffs that were as good if not
better than their competitor quickly differentiated itself from the other rules.
Similarly, poor performing rules suffered such major degradations in their
strength that their poor performance was unlikely to be repeated.
Learning classifier systems rely on the use of genetic algorithms to
produce the new rules needed to improve the quality of their rulebases (which
typically contain only a small subset of the total number of rules). In the current
system, a genetic algorithm was used every 50 rounds to replace four weak
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rules with four new rules. Each rule to be replaced was selected by taking the
rule with the lowest strength from a random sample of 10 rules in the rulebase.
Each new rule was generated by combining information from two parent
rules. Roulette wheel selection was used to bias the selection of parents towards
stronger and fitter rules; the probability of selection being proportional to the
strength of each rule. The rules of the two parents were then combined via
crossover to form the child rule. A dual crossover technique was used with two
crossover points being selected: one in the condition component, one in the
action component. The father‟s rule was used up to the crossover point, the
mother‟s rule after the crossover (see Figure 4.6). Following crossover, each bit
position in the new rule had a small (1%) chance of being „mutated‟ to a new
value (randomly selected from 0,1, or # in the condition component or 0,1 in the
action component). The rule strength of the child was then calculated a weighted
average of the parents‟ strengths: where p was a random number between 0 and
1.
3
note that wildcards were allowed in the condition component, so the condition 1#1
could match 111 or 101.
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itness Landscape
6 5 4 3 2 1
Figure 4.15 Example of dual crossover
0
5
130
10
Classifier System
The classifier system strategy used the same bid and reward sub-systems
as the learning classifier system but lacked the genetic algorithm used by the
learning classifier system to generate new rules. The classifier system strategy
was richly compensated for this apparent deficiency by being allocated a
rulebase that included every possible rule combination in the game (a total of 64
rules). In essence, search was not required because the classifier system‟s
rulebase was fully-specified with every possible solution in the problem space.
It was hypothesised that the classifier strategy would dominate the
learning classifier strategy because it would not have to engage in a search
process to locate an optimal strategy. Instead, the classifier system‟s reward
process was expected to quickly differentiate the best strategy from the
population of candidates held in its rulebase without having to wait for the best
strategy to be born. However, on occasion, a learning classifier system could
perform as well as a classifier system if it had the good fortune to have the
optimal strategy (or strategies) already present in its rulebase.
In large problem spaces, fully specified classifier systems are
impracticable because the size of the rulebase would exceed memory and
computational capacity. Their inclusion in this small demonstration game is
intended to highlight the ability of a learning classifier system to perform well
with only a small subset of the full rulebase. A comparison between the
performance of classifiers and learning classifiers is a good measure of the
latter‟s relative efficiency.
Aspirational Feedback System
A significant line of simulation reseach in the organisation and
management literature has relied on cybernetic feedback systems combined
with aspiration levels to guide the adaption of agents in the models of interest
(Cyert & March, 1963; Lant & Mezias, 1990; Mezias & Eisner, 1995). In these
systems, the external performance of an agent was compared with an internal
131
aspiration level. If performance was below aspiration then the agent was
motivated to change its behaviour. If performance exceeded aspiration then the
agent persisted in its behaviour while adjusting its aspiration level upwards.
In the current game, players that used the aspirational feedback strategy
started with an action of „000‟ and an aspiration level of 0.1. This provided an
immediate incentive for change as performance fell below the aspiration level.
At any stage when performance fell below the aspiration level, each of the three
positions in the action string had a 33% chance of being flipped (i.e. from 0 to 1
or from 1 to 0). Eventually, a state was found where performance exceeded the
initial aspiration level of 0.1. This state was carried over into the next round,
and the aspiration level adjusted upward according to the formula:
Over time, the combination of rising aspiration levels and random search
produces a system that gravitates towards higher levels of performance.
Moreover, changes in the environment that cause performance to fall below the
system‟s aspiration level lead to an immediate search for new solutions. Thus,
the system is stable when performing well and responds rapidly in the case of
poor performance.
The use of aspirational feedback systems to motivate artificial adaptive
agents has had a long and respectable tradition in the organisation and
management literature. However, learning classifier systems had at least two
initial advantages over aspirational feedback systems:
1. Contingent Framework. The use of a rule-based framework explicitly
assumes that actions are contingent on the state of the environment. Like
humans, classifier systems react intelligently to variations in the environment.
In contrast, aspirational feedback systems must search blindly to locate new
actions that produce performances in excess of aspiration levels.
132
2. Transparency. The rules that evolve in the rulebase of a learning
classifier system can be directly interrogated for theoretical significance. There
is no corresponding ability to interrogate aspirational feedback systems.
In this tournament, we were keen to demonstrate that a learning classifier
system could perform as well, if not better, than a more traditional adaptation
mechanism such as aspirational feedback. If learning classifier systems could
perform well vis-a-vis other methods then a strong case could then be made for
their use in future agent-based simulations.
Each time a classifier system detects a new environmental state it tries to
learn the best action to take to maximise performance. A history or „memory‟
of this learning experience remains in the rulebase even after the environment
has changed. We hypothesised that this „memory‟ may help a classifier system
to outperform an aspirational feedback system. Aspirational feedback systems
must search blindly for better strategies when their environment changes.
Conversely, classifier systems can remember how to act in an environment that
is the same or similar to states already traversed. Classifier systems can thus
react more rapidly. This should translate into a higher average performance
over time.
Random Walk
The random walk was considered the default strategy in the game.
Starting with an action of „000‟ there was a small probability (5%) of each bit
in each round being flipped (i.e. from 0 to 1 or from 1 to 0). Thus, a side
utilising this strategy undertook a random walk through all possible actions in
the game with no regard for performance.
It was hypothesised that the three adaptive strategies outlined earlier
should clearly outperform the random walk if they were in any sense effective
algorithms. Randomly selecting an action with no regard for performance
should represent a lower bound on performance in the game.
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Results
At the end of each 200-round simulation run, the performance difference
between each strategy was calculated as the cumulative performance of Player
A less the cumulative performance of Player B. The average performance
differences for each combination of strategies can be found in Table 4.2. The
table also contains an indication of the significance of a Wilcoxen signed rank
4
test conducted on each performance differential . The null hypothesis of the test
is that the performance differential is zero. Significant departures from zero
were noted in most cells.
Several observations can be drawn from Table 4.2. First, the classifier
system strategy (2) performed extremely well against all other strategies; with
the learning classifier system (1) managing to post the best performance versus
the classifier system strategy. Secondly, the learning classifier system on average
performed better than the aspirational feedback system (4) (significantly so as
Player A but not as Player B).
Table 4.5 Mean performance differentials
Player B-> 1
2
3
4
Player A
1
1.38
-30.47** 78.36**
10.59*
2
33.19**
-5.01
105.99** 84.67**
3
-84.22** -107.95** -2.62
-69.40**
4
-4.89
-77.07** 68.44**
-0.39
1- Learning Classifier System, 2- Classifier System, 3- Random Walk, 4 - Aspirational
Feedback System, * - p<0.05, ** - p<0.01 (n=128 for each cell)
Several other results attest to the robustness of the findings. For example,
the performance differential of each strategy when played against itself was
restricted to single digit differences and in all cases was never significantly
different from zero. This suggests that the code for Player A mirrors that for
Player B. Secondly, the random walk strategy (3) was outperformed by every
4
A nonparametric test was used when the data failed to meet the standards of normality
required for parametric tests.
134
other adaptive strategy as predicted. Clearly, the adaptive algorithms perform
significantly better than chance on this task. However, this result also suggests
that gross errors were not made when translating the algorithms into computer
code. While errors may still exist in the code, they were not serious enough to
disrupt the basic operation of each algorithm.
Comparing Human and Artificial Agents
An additional set of simulation runs was undertaken to compare the
performance of artificial agents with human subjects. Each combination of the
three adaptive strategies (excluding random walk) was run through 100 trials of
100 rounds. For each step in the simulation, the average proportion of cases
where both simulated players were using the dominant strategy of 111 was
recorded (see Figure 4.7).
Figure 4.16 Proportion of artificial agents using dominant strategy
The behaviour of the learning classifier systems (1) and classifier system
(2) pair has the closest similarity to the data collected from human subjects (see
Figure 4.5). In both cases, the use of the dominant strategy grows over time as
players learn the game. By the end of the game, almost 40% of artificial agents
and around 70% of human subjects were using the dominant strategy. While
this discrepancy between artificial and human subjects appears large, it masks
the fact that many artificial agents were able to achieve a stable co-operative
strategy. Moves such as 101 and 110 for Players A and B respectively yielded
payoffs of 0.4 for both players as opposed to a payoff of only 0.3 for the
dominant strategy of 111. A significant proportion of the classifier systembased artificial agents were able to co-operate to achieve a higher payoff,
pushing the proportion of artificial agents finding a stable strategy to around
70%.
Games played with the aspirational feedback strategy (1v4, 2v4) show no
growth in the use of the dominant strategy over time. The aspirational feedback
135
system has a 1 in 64 chance of randomly finding the dominant strategy in any
particular round. The classifier systems will only learn to play the dominant
strategy after it has been discovered by the aspirational feedback system. Up to
that point, other actions will earn higher returns. The probability of both sides
playing a dominant strategy is thus quite low and remains relatively constant in
each round.
The graphical results also show that classifier systems (2v4) have a
higher proportion of dominant strategy moves than learning classifier system
(1v4) when playing aspirational feedback systems. Once the aspirational
feedback system locks onto a dominant strategy (111), its opponent must search
for the best response (also 111). Classifier systems can find this very quickly
because all possible rules are available. However, many learning classifier
systems will not have the appropriate rule (111->111) in their rulebase and
must wait for the genetic algorithm process to locate the appropriate move. In
the short context of the game, this might never occur, hence generating the
lower proportion of dominant strategies.
Conclusion
This chapter introduced an adaptive algorithm for artificial agents known
as a learning classifier system. Like an expert system, a learning classifier
system comprises a set of production rules that allow a contingent response to
the environment and a degree of transparency in the operation of rules.
Whereas an expert system relies on a human expert to create and maintain its
knowledge base, a learning classifier system employs machine learning
techniques to automatically improve the quality of rulebase without the aid of a
human expert. In particular, it uses the biological mechanisms of crossover and
mutation to produce newer, and hopefully better, rules from the fittest existing
rules in its rulebase. The relative fitness of a rule is determined via an operant
conditioning system which rewards rules for good performance and punishes
rules that perform poorly.
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The learning classifier system was pitted in a tournament against three
other strategies in a contest based on a simple strategic game. The learning
classifier system was only out-performed by a fully-specified classifier system.
Although this result was predictable, it had few practical implications because
it was recognised that a fully-specified classifier system was impracticable for
larger problems. More importantly, the learning classifier system performed as
well, if not better, than an aspirational feedback system. The latter strategy
represented one of the most popular methods for motivating artificial agents in
previous microsimulations in the organisation and management literature. This
result, combined with a propensity for classifier systems to mimic human
learning patterns, provides our justification for using learning classifier systems
to motivate artificial agents in more comprehensive microsimulations of
strategic phenomena. A task we describe in the next chapter.
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Creating Strategic Landscapes
"Any effort to understand success must rest on an underlying theory of the firm
and an associated theory of strategy. While there has been considerable progress
in developing frameworks that explain differing competitive success at any given
point in time, our understanding of the dynamic processes by which firms
perceive and ultimately attain superior market positions is far less developed."
- Michael Porter
The central thesis of this dissertation was that a set of artificial adaptive
agents evolving on a complex strategic landscape could provide us with
insights into the theory and practice of strategic management. In the previous
chapter, we introduced an algorithm capable of motivating artificial agents to
discover strategies yielding higher levels of perfomance in a simple strategic
problem. This chapter describes the method that was used to create a more
complex environment for artificial agents to explore. Moreover, as the building
blocks for this complex environment were drawn from recent theories in the
strategy literature, we would argue that the behaviour of artificial agents in
such an environment has some intrinsic interest to strategists. The next chapter
discusses the results from three representative experiments that showcases the
ability of the model to engage in a range of theoretical inquiries.
The opening section of this chapter discusses the specifications of this
complex strategic landscape based on the theoretical considerations introduced
in Chapter Two. This is followed by a discussion of the interface between this
new environment and the learning classifier systems introduced in the previous
chapter. The final section discusses the broad strategies that agents must follow
in order to be successful in the complex environment that has been created.
Constructing a Strategic Landscape
The process of constructing a strategic landscape requires certain
assumptions to be made about the nature of objects in the environment and the
relationships between those objects. Taken together, it was these relationships
138
that generated a strategic landscape for our artificial agents to explore.
Specifying the nature of objects and their relationships has been termed the
ontology of a simulation model.
In philosophy, ontology is the study of the organisation and nature of the
1
world independent of our knowledge about it (Guarino, 1995) . Ontology is
concerned with the nature of objects in the world and the relations between
those objects. It has been described by Guarino (1995) as “...a theory of a priori
disinctions” (p. 628). Basic philosophical questions in ontology include “What
classes of things exist in the world?”, “What are the properties of things?”,
“How do we know that two things belong to the same class?” and “How does a
property differ from a thing?”.
The term, ontology, has acquired a slightly different meaning in the field
of artificial intelligence where it refers to the basic conceptual categories from
which models of the world are made. “An agent (e.g. an autonomous robot)
using a particular model will only be able to perceive that part of the world that
his ontology is able to represent. In a sense, only the things in his ontology can
exist for that agent. In that way, an ontology becomes the basic level of a
knowledge representation scheme” (Heylighen, Joslyn, & Turchin, 1995).
An ontological decision had already been made in the current study to
divide the model into agents, on the one hand, and the “environment” or
strategic landscape, on the other. While this approach has strong support in the
artificial intelligence literature (Clancey, 1993) it is by no means the only
conceptualisation of the world available. Systems theory, for instance, assumes
that every “thing” in the world is a system and that every system comprises part
of a larger system (Heylighen et al., 1995). System dynamics, an approach to
simulation derived from systems theory, divides objects in the world into only
two categories, called stocks and flows (Senge, 1990). From this perspective,
classifying objects in the world as either “agent” or “environment” is an
artificial distinction that tends to obscure the essential oneness of all things.
139
In the past, these different images of organisation have been a source of
conflict in organisation and management theory (Morgan, 1997). Explanations
derived from contingency theory, for instance, differ markedly from those
offered by Marxist or institutional theory. These differences were at least partly
attributable to different ontologies. While some authors view this diversity as a
weakness in organisation science (Pfeffer, 1993), we prefer to see different
world-views (and associated ontologies) as complementary. Researchers
viewing different approaches as complementary have the freedom to choose a
particular ontology and methodological toolkit that they believe is most
appropriate to increase knowledge and understanding in a particular problem
area. However, in an act of ontological opportunism, we resolved to develop no
new conceptual categories of our own; relying instead on the four categories
2
used by Amit and Schoemaker (1993) in their integrated theory of competitive
advantage (see Figure 2.3).
There were a number of advantages to this approach. First, the categories
were deeply grounded in the theoretical traditions of the strategic management
discipline, and were thus intuitively appealing to both academics and
practitioners. Secondly, Amit and Schoemaker‟s theory represented an attempt
to integrate two major streams of research in strategic management; namely the
resource-based and industry-based views. Such integrated models had been
shown to have more explanatory power than a single theoretical perspective
(Roquebert et al., 1996; Rumelt, 1991). Finally, the use of Amit and
Schoemaker‟s four categories did not preclude the introduction of dynamic or
evolutionary factors into the analysis. To suggest that firms move in and out of
industries over time, or that the stock of strategic assets might vary over time,
was not fatal to the ontological distinctions being drawn and could even be
viewed as a natural extension of the original model.
1
2
whereas epistemology is the study of what can be known about the world
firm, industry, strategic industry factor and strategic asset
140
Objects in the simulation
The conceptualisation and operationalisation of Amit and Schoemaker‟s
theory as a simulation model is discussed in the following two sections. The
first section focuses on a description of the objects in the simulation and their
properties, while the following section concentrates on the relationships and
interactions between the objects in the simulation and the means by which
these interactions create our strategic landscape. A conceptual overview of the
model is presented in Figure 5.1. Note the use of matrices to store data about
the relationships between objects in the simulation.
141
ess Landscape
5 4 3 2 1
Figure 5.17 Conceptual overview of simulation model
0
5
142
10
20
Industry
In simulations of biological systems (often termed artificial life), the
location and amount of food and other resources have often played an
important role in determining the shape of a fitness landscape (Levy, 1992). A
geographical area with a large food supply would typically be able to support
more life relative to another area with less food. Biologists, and more recently
population ecologists, have referred to these areas as niches in the environment
(McKelvey, 1994). Niches vary in their capacity to support life. Munificent
niches (ie. those with a high level of food or other resources) have a greater
3
capacity to support life and appear as peaks on a virgin fitness landscape .
In the current study, the concept of industry has been used to demarcate a
strategic fitness landscape in much the same way that geographical niches have
4
helped to demarcate a biological fitness landscape . The strategic management
literature has provided strong empirical and theoretical support for the notion
that firm performance is at least partially dependent on industry membership
(Amit & Schoemaker, 1993; Porter, 1980; Porter, 1985; Rumelt, 1991;
Schmalansee, 1985). Moreover, industry effects exist independently of firm
effects (Roquebert et al., 1996). Each industry has a different capacity to
support profit-making. This is manifested by the appearance of peaks and
troughs on an industry-based virgin fitness landscape, which is a visual
representation of the profit potential of each industry in an economy (see Figure
5.2).
3
A virgin fitness landscape has zero population. Competitive pressures may deform a
fitness landscape from its virginal state. A niche that easily supports ten rabbits may struggle
with a population of a thousand rabbits. Co-evolutionary pressures from other species can also
differentially affect the relative fitness of a niche.
4
It should be noted that accepting the primacy of industry-based environmental
variations does not imply a rejection of geographical factors. While industry membership has
been identified as the primary source of environmental fitness variations, it is not the only
source. Future researchers may wish to expand the model by including more sources of
environmental variation. Geographical factors are an obvious candidate.
143
ithout competition)
ess Landscape
Figure 5.18 Sample fitness landscape for twenty industries
5 4 3 2 1
0
5
144
20
Porter (1980) has strongly advocated the view that manager‟s have a
choice in industry selection. Like geographical niches in the biological world,
industries in the commercial world vary in their level of munificence.
Therefore, ceteris paribus, industry selection has the capacity to enhance (or
degrade) a firm‟s performance relative to the system-wide average.
Up to this point, we have resisted explicitly defining our concept of
industry. In real world studies, the determination of industry and market
boundaries has often proved problematic (Brooks, 1995; Curran & Goodfellow,
1990). The use of standard industry classifications, measures of cross-elasticity
of demand, and differences in consumer behaviour are just some of the
methods that have been proposed for drawing boundaries between industries. In
an artificial world, drawing boundaries is much easier. In a formal sense, each
of our industries was created to have a zero cross-elasticity of demand with
every other industry. Thus, firms in a particular industry were insulated from
the actions of those in other industries. Industries formed the locus for
competition meaning that firms could not affect the profit outcomes in
industries in which they did not operate.
Strategic Industry Factors
In the previous section, it was argued that industries varied in their
munificence, with some industries having a greater profit potential than others.
Amit and Schoemaker (1993) defined strategic industry factors, or SIFs, as
“...the key determinants of firm profitability in an industry...” (p. 36).
Consequently, in our simulation model, the label „strategic industry factor‟ was
applied to those industry characteristics that created variances in industry
munificence. An industry with more strategic industry factors had a greater
capacity to extract surplus value than an industry with fewer strategic industry
factors.
The notion that there are factors at the industry level that affect firm
profitability has received recent empiricial support. Montgomery and Hariharan
145
(1991) hypothesised that the performance of a particular firm within an
industry would be determined by the joint interaction between the
characteristics of the industry and the characteristics of the firm. In their study,
Montgomery and Hariharan (1991) described several industry characteristics,
including industry growth, research and development intensity, economies of
scale and firm concentration. These characteristics were found to co-vary with
firm profitability.
Strategic industry factors (SIFs) were operationalised in a simple manner
in the simulation model. At the start of each simulation run, the number of SIFs
was fixed at a finite level (which was controlled via a global parameter). While
it was recognised that the population of SIFs changed in the real-world, the
assumption of a fixed number of SIFs greatly reduced programming
complexity. However, this was considered a minor limitation. When the
number of SIFs in a simulation was large, only a small subset were active in a
given industry at a given time. Thus, although SIFs were drawn from a fixed
menu, the number of SIFs in a simulated industry changed over time.
During the initialisation phase of the simulation, a random number of
strategic industry factors were activated in each industry. Any particular SIF
had a probability, pB, of being active in a particular industry. The size of an
5
industry was a metric of its profit potential or ability to generate surplus value .
Industrial organisation economists have attributed the ability of firms in a given
industry to earn above-average returns (or economic rents) to the structural
characteristics of the industry (Conner, 1991). Accordingly, industry size was
defined as a count of the number of active SIFs in an industry. This followed
Porter‟s (1980) argument that strategic industry factors could be conceived as
impediments to the action of competitive forces. The more factors impeding
competition, the higher the available rents.
As a simulation progressed, the relationship between industries and
strategic industry factors could be altered. Each round, there was a small
146
probability, probB, that a particular SIF would cease to be active in an industry.
There was also an equal probability that a previously dormant factor would
become active in an industry. Thus, a degree of uncertainty or turbulence could
be simulated at the industry level. This had the effect of changing in the „rules
of the game‟ for firms in the industry. By increasing the value of probB,
turbulence in an industry could be increased.
Firm
The concept of the firm as a profit or rent-seeking entity has been the sine
qua non of strategy content research. For the purposes of the simulation model,
a firm was defined as a profit-seeking entity that learned from its environment
in order to seek higher levels of performance. As such, the firm represented the
“locus of agency” for our agent-based models.
Firms were assumed to encode their knowledge in the form of production
rules (Egidi & Marris, 1992; Newell & Simon, 1972). We have previously
discussed how human experts often codify their knowledge of the world as
statements of the form “If condition X then action Y”. Previous researchers
have demonstrated that organisational knowledge can also be encoded as
production rules (Baligh et al., 1986; Cyert & March, 1963; Masuch & LaPotin,
1989). Therefore, it was convenient to view the firm as the keeper of a
knowledge base that comprised the accumulated organisational knowledge of
the firm in production rule form. The learning activities of the firm thus had the
effect of producing change in its knowledge base.
It should be noted that the decision to attribute agency to firms was not
intended as an anthromorphisation of the firm in any way. In the study of
organisational learning, most theorists accept that individuals are the locus of
learning and transformation (Dodgson, 1993). Certainly, theories of
organisational learning use metaphors drawn from individual learning.
However it should be noted that:
5
Or, more precisely, net cashflow before cost of capital
147
“Although organizational learning occurs through individuals, it would be a
mistake to conclude that organizational learning is nothing but the cumulative
result of their members' learning. Organizations do not have brains, but they have
cognitive systems and memories...Members come and go, and leadership
changes, but organizations' memories preserve certain behaviours, mental maps,
norms, and values over time” (Hedberg, 1981, p.3)
In the decision routines of our simulation model, there was an explicit
tension that was resolved within each firm over the choice of possible actions
(or rules) to use. The method the simulation used to resolve this tension was
not inconsistent with the dominant logic (Prahalad & Bettis, 1986) or garbage
can (Cohen et al., 1972) views of strategic decision-making. Both theories
maintain that firms are composed of individuals and groups with heterogenous
preferences. In the garbage can model, decision-makers come to choice
situations with a variety of solutions to problems. As new people are brought
into the firm, new solutions and new problems also emerge. In the garbage can
view, no outcome to a decision is preordained. The outcome is a function of the
individuals, problems and solutions that are drawn into a choice situation (the
„garbage can‟).
While it is possible to cite cases where unusual or remarkable decisions
were made by organisations because of the particular dynamics of a choice
situation, most critical decisions in an organisation are taken within a preexisting power structure and are heavily biased towards certain outcomes. The
dominant logic paradigm holds that resource allocation is usually undertaken
by a collection of key individuals, also known as the top management team or
dominant coalition. This top management team will typically have a preferred
set of routines (or actions), known as the dominant logic, that is developed
from past experiences. Prahalad and Bettis (1986) explicitly refer to operant
conditioning as the mechanism that creates this dominant logic:
“The repertoire of tools that top managers use...is determined by their
experiences. A dominant logic can be seen as resulting from the reinforcement
that results from doing the right things...they are positively reinforced by
economic success” (pp. 490-491).
Thus, the firm can be conceptualised as a collection of rules, reinforced
by past experiences, that drives future decisions. While novel rules are
148
constantly being introduced into the organisation, successful rules are much
more likely to be used in determining the firm‟s behaviour. Knowledge of these
rules are carried by individuals, particularly the top management team, but
rules are also independent of individuals and can be transmitted over time
through the organisation‟s culture and norms (Schein, 1985). The removal of
the top management team does not necessarily imply that their rulebase has
also been deleted from the organisation. However, negative experiences with a
particular dominant logic can be expected to quickly extinguish maladaptive
behaviour; initiating a search for more successful actions.
Properties of the Firm
Firms in the simulation were comprised of three components:
• a set of industry memberships
• a set of strategic assets
• a knowledge base operationalised as a learning classifier system (see section
5.2)
Strategic Assets
Strategic assets (SAs) are “...the set of difficult to trade and imitate,
scarce, appropriable and specialised resources and capabilities that bestow the
firm‟s competitive advantage” (Amit and Schoemaker, 1993:36). In the
simulation, firms selected combinations of strategic assets from a fixed menu
of possibilities.
As Amit and Schoemaker‟s concept of “strategic asset” was derived
directly from the resource-based view of the firm, this rich body of theory
proved useful in defining some of the properties of a strategic asset (see also
Section 2.4). First, it was recognised that a strategic asset would have
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acquisition, maintenance and exit costs like any other normal asset (Barney,
1986a). However, rather than creating a „strategic factor market‟ as suggested
by Barney (1986b), the notion of asset cost was highly simplified in the
simulation model. An asset charge, notionally representing a „cost of capital‟
covering acquisition and maintenance costs, was uniformly applied to each
asset. Each asset was assumed to have a value or weight of „1‟ with the asset
charge being some fraction of this amount (usually 10%). Thus, a firm with six
assets would typically be charged 0.6 as an asset charge.
Early versions of the simulation model assumed that cost was the only
barrier to acquiring a strategic asset. According to the resource-based literature,
cost is not the only barrier to acquiring strategic assets. The ease of imitation,
or inimitability, of an asset is a central theoretical construct in the resourcebased view (Barney, 1991). Other authors have pointed to the saliency of time
lags in acquiring assets and the importance of being „in the right place at the
right time‟ (Dierickx & Cool, 1989; Teece et al., 1994). Furthermore, the
development of at least some strategic assets relies on the existence of prerequisite or co-requisite strategic assets that are not possessed by all firms.
Thus, Harvard‟s ability to attract excellent faculty was partly a function of its
reputation, acquired over a lengthy time period, and difficult, if not impossible,
to imitate. Later versions of the simulation model attempted to incorporate
these observations. These modifications or departures from the base model are
described in detail in the method sections of experiments two and three in the
next chapter.
Fundamentally, the term „strategic asset‟ was used in the simulation to
refer to the resources and capabilities possessed by the firm that enabled it to
obtain a share of rents available in one (or more) industries. That is, strategic
assets were distinctive from common assets because they contributed to
resource share in an industry (Wernerfelt, 1984). In the Harvard example,
reputation is a valuable asset for the university because students are willing to
pay above-average returns to a university with a good reputation. The
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simulation model assumed that all firms that possessed the same strategic asset
would receive an equal benefit. As reputations of universities vary (even in the
Ivy League) this was an obvious simplification. However, the approach reduced
complexity and enabled faster coding of the model using the learning classifier
system interface. Future research may explore the implications of variable
weights for strategic assets.
Relationships between objects
The simulation model used binary-coded matrices to represent the
relationships between objects in the simulation (see Fig 5.1). The use of
matrices to represent strategic relationships had been suggested by a number of
authors including Wernerfelt (1984) and Schoemaker and Amit (1994).
However, the original inspiration to use binary-coded matrix representations
was generated after reading about the „garbage can‟ model (Cohen et al., 1972).
The garbage can model was one of the first to demonstrate that important
theoretical insights could be gained from the study of abstract minimalist
models of organisation.
Four matrices were used in the current model. The industry membership
matrix (or matrix A) was used to store the industry affiliation data for each firm
in the simulation. In the matrix, the element aij represented the status of firm i
in industry j. A value of „1‟ in aij indicated that the ith firm competed in the ith
industry, while a value of „0‟ indicated nonparticipation. At the start of each
simulation, the matrix was initialised with zero values. In this way, no
advantage could be acquired from a favourable starting position (ie. firms
started with no industry affiliations).
The relationships between industry and SIFs were stored in the industry
structure matrix (or matrix B ). A value of „1‟ for the element bij indicated that
the jth strategic industry factor was active in the ith industry, while a value of 0
indicated the relative unimportance of the factor in that respective industry.
Thus, a completed industry structure matrix might have taken the form:
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The asset holdings of a particular firm were summarised in the asset
ownership matrix (or D matrix). The symbol „1‟ in dij th element of the matrix
represented the acquisition or development of the jth strategic asset by firm i..
The symbol „0‟ indicated that a firm did not hold a particular asset.
As the simulation progressed, the various objects in the simulation
interacted to produce a number of outputs from the model. The primary output
of this interaction was a measure of firm performance that served as a proxy for
economic profit. Before profit was determined in the model, an intermediate
step needed to be undertaken to determine the „fit‟ between firm‟s strategic
assets and the requirements of an industry. Consequently, in the remainder of
this section, we shall examine the notion of fit in the model and then turn our
attention to the method of calculating profit.
Fit
The following quotation from Spender (1992, p. 6) captures the centrality
of „fit‟ in the Zeitgeist of strategic management:
“Optimum fit equates to maximum profit and, by assumption, needs no further
justification. This [SWOT] model, sometimes called the "alignment" model,
dominates the teaching and research of strategy. It takes all the issues that might
upset the firm's progress toward its goals, whether they occur within the firm or
within its environment, and relocates them at one or the other of these
interfaces.”
The concept of fit in the simulation was operationalised by introducing a
fourth matrix, the fit or C matrix, into the model (see Fig 5.1). A „1‟ in cij th
element of the strategic fit matrix (C), indicated the alignment of the ith
strategic industry factor with the jth strategic asset. For example, an industry
with large economies of scale (a strategic industry factor predisposing an
industry towards higher economic returns) favours those firms that invest in
factories of minimum efficient scale (a strategic asset). Similarly, if the ability
to differentiate a product was a strategic industry factor then building strategic
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assets such as product quality, customer service and branding would „fit‟ the
industry and theoretically provide higher returns. Thus, there is a one-to-many
relationship between strategic industry factors and strategic assets. The reverse
may also be true. According to Porter (1980), a strong brandname will enhance
product differentiation but also raise switching costs and erect barriers to entry.
Our operationalisation of fit has allowed many-to-one and one-to-many
relationships between strategic industry factors and strategic assets.
Profit
Within the current study, higher performance was equated with a higher
share of relevant strategic assets within an industry. Substituting resource share
for market share was based on Wernerfelt‟s observation that “...resources and
products are two sides of the same coin. Most products require the services of
several resources and most resources can be used in several products."
(Wernerfelt, 1984):171. The configuration of the asset ownership matrix (D)
was thus crucial in determining relative performance in an industry.
Performance for the ith firm in the jth industry at time t was calculated
with the formula:
The summation term on the right merely represented the size of the
industry (as a count of the number of SIFs). Determining resource share (RS)
required additional matrix manipulation (the time subscript has been dropped
in subsequent formulas to reduce clutter).
Determining Resource Share (RS)
The following intermediate matrices were created in the performance
routine:
E
(nInds.nSAs)
=
B
(nInds.nSIFs)
x
C
(nSIFs.nSAs)
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F
=
(nFirms.nInds)
D
(nFirms.nSAs)
ET
x
(nSAs.nInds)
The matrix E counted the number of times each strategic asset aligned
with strategic industry factors in a particular industry. If a strategic asset was
aligned with more than one strategic industry factor then its value in the E
matrix was greater than one.
The F matrix examined the strategic assets held by each firm and counted
the number of matches in an industry assuming that the firm competed in all
industries. The F matrix thus represented the theoretical maximum number of
matches for a given strategic asset portfolio. By applying the industry
membership matrix (A) to the F matrix, false matches were eliminated to create
a new matrix, M. Resource share was then calculated as the ratio of firm
matches to total matches in the industry:
Multiplying this value by industry size yielded a gross profit for each
industry.
Net firm performance was determined by taking the summation of
performance across all industries and deducting a charge for the strategic assets
held by the firm. The following equation depicts the net performance for the ith
firm:
(4.5)
Although the level of asset charge could conceivably vary by asset type, a
common asset charge (AC) of 0.1 was used in the current model. Without an
asset charge, the optimal strategy for each firm was to enter all industries and
acquire all strategic assets. When a charge is present, firms must consider if the
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potential performance gain of each new asset, including the opportunity for
economies of scope across industries, will exceed the cost of that asset.
A Worked Example
The size of both industries is two units. The gross profit of the first firm
would be =0.67 units in the first industry and 2 units in the second for a total
gross profit of 2.67 units. The second firm would receive =1.33 units from the
first industry and zero from the second. After deducting an asset charge of 0.1
units per strategic asset, the firms are left with net profit of 2.57 and 1.13 units
respectively.
The Agent-Environment Interface
This section examines the method used to adapt the learning classifier
system presented in Chapter 4 to the complex environment outlined above.
While the basic operation of the algorithm was not altered, adjustments had to
be made to the structure of the rule and message sub-system and the reward
sub-system. These changes are described below.
The Rule and Message Sub-System
Firms, or agents, in the simulation received environmental information
solely from the industry membership (A) and asset ownership (D) matrices. As
these matrices were already coded in binary form, no conversion was required
to make the information usable by the classifier system. Raw states were fed
directly into the system.Consequently, firms were ignorant about the
relationship between industries and strategic industry factors (Matrix B) and
the fit between strategic industry factors and strategic assets (Matrix C). Firms
were required to induce these relationships from the patterns of industry
membership and asset ownership.
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The firm‟s knowledge was split into two rulebases - industry rules and
resource rules. The condition component of the industry rulebase used
information from the industry membership matrix to make decisions about
changes in the firm‟s own industry membership profile. A typical rule took the
form:
Wildcard symbols (#) were used in both the condition and action
component of the rules. In the condition component, a wildcard stood for any
symbol. Thus, the condition #00 could match multiple inputs (ie. 100 and 000).
In the action component, the wildcard symbol meant „do nothing‟. A firm in an
initial state of 101, when sent the action 0#1, would alter its position to 001;
the wildcard symbol simply „passing through‟ the old value.
In the resource rulebase, information from the asset ownership matrix
was matched against elements in the condition component. Each action
component of a resource rule acted on the firm‟s position in the asset
ownership (or D) matrix to change its portfolio of strategic assets. As with the
industry rulebase, a typical rule took the form:
The wildcard symbols performed the same functions as in the industry
rulebase.
The Reward Sub-System
The active rule was rewarded, typically in the ratio of 10:1, for the
absolute magnitude of performance and the change in performance levels
respectively. The active rule was rewarded according to the following formula:
The values of alpha and beta were typically 10 and 1 respectively.
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On occasion, it was desirable to reward not only the active rule but also
the chain of rules that set up success in the current round (Holland & Miller,
1991). Accordingly, the previous five active rules shared in the reward of the
current active rule according to the following algorithm:
where was the active rule in round t-x and R is the change in strength of
the current active rule,
Other sub-systems
Other parts of the learning classifier system operated as described in
Chapter 4:
1.
Incoming messages were matched against the condition
components in the rulebase.
2.
Matching components bidded for their rule to be activated. Bid
6
strength was a function of rule strength, rule specificity and a
random Gaussian error.
3.
The rule with the highest strength (in each rulebase) used its
action component to alter the firm‟s environment.
4.
Rules were rewarded (or punished) according to the success of
their action as measured by firm performance.
5.
Periodically, successful rules were genetically recombined to
introduce new, hopefully fitter, rules and remove less successful
rules.
6
A „fuzzy‟ matching system was introduced in experiment two in the next chapter.
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Game Strategy
The strategic game created in this chapter presents a complex problem to
players attempting to maximise profits. They must choose a combination of
industry memberships and assets that will yield positive, and hopefully,
increasing returns.
Ashby
M iller
Performance
C ognitive C apacity
Figure 5.19 Strategic landscape for one firm
The difficulty of this problem can be illustrated by considering the fitness
landscape faced by just one firm playing a game with two strategic assets and
two industries (see Figure 5.3). In this game, there are four combinations of
industry membership and four combinations of asset ownership. Even this
apparently simple example is capable of generating a rugged fitness landscape
with several hills and valleys. It is conceivable that a firm could find itself in a
local optimum on the lower of the two ridges and then have trouble migrating
to the global optimum (the peak on the right and to the rear of the diagram). In
fact, given that the firm can only learn about the shape of a fitness landscape
through experience, it might never discover that higher areas of fitness even
exist. Thus, firms can easily get trapped in a local optimum; becoming in a
sense a victim of their own ignorance and comfort with the status quo.
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The problem becomes much more difficult when competitors are
introduced into the game. This creates what Kauffman has called a „dancing‟
fitness landscape (Kauffman, 1988). Firms are forced to compete in an
evolutionary arms race which, from the perspective of a given firm, has the
effect of continuously distorting the fitness landscape. Firms are thus said to be
co-evolving on a fitness landscape.
Strategy on a co-evolving strategic landscape is not a matter of seeking
and then striking out for the peak with the highest fitness. Chances are that the
actions of competitors will have caused the peak to have disappeared before it
is reached. Rather, strategy in this type of environment is about a process of
mutual adjustment. Excessive and overzealous competition in a market will
depress profits. Firms must learn to find an equilibrium position or
evolutionarily stable strategy (Maynard-Smith, 1982) that will allow a tolerable
amount of profit sharing (and thus survival) for each competitor. Such
adjustments are more likely to be evolutionary and incremental rather than
radical and revolutionary (Bak & Chen, 1991; Tushman & Anderson, 1986).
It is in this context that we have sought to identify empirical
generalisations from the model. We believe asking questions such as “Do some
firms perform better than others” and “What strategies perform best in this type
of fitness landscape” are interesting and legitimate questions for strategy
research(ers). Moreover, we believe the answers to these questions have the
potential to increase understanding of strategic behaviour and inform practice.
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Studying Strategic Landscapes
“We seek excellence, not perfection”
- Stewart Kauffman, At Home in the Universe
According to NASA, a testbed is “...an environment in which a critical
experiment, observation or evaluation occurs under controlled conditions and
1
operations.” Interestingly, NASA cites engineering models and simulations as
examples of testbeds. Using this definition, we argue that the model developed
in the previous chapter is a type of testbed; a testbed capable of studying a
variety of problems in strategic management. In recognition of this fact, we
have christened our base model or “simulation environment” as SELESTE
2
(StratEgic LEarning TEstbed) .
In this, the penultimate chapter, we present the results of three
representative experiments. These experiments are representative in the sense
that, while not an exhaustive demonstration of SELESTE‟s flexibility, they
enable us to showcase a variety of categories of strategic problems that
SELESTE is capable of addressing. However, while these are demonstrations
of SELESTE, they are also substantive studies in their own right. The first
study investigates the effect of cognitive capacity on performance by studying
whether, ceteris paribus, firms with larger knowledge bases outperform firms
with less information. This is followed by an experiment on the environmental
conditions that lead imitation to outperform innovation as a competitive
strategy. The final experiment focuses on the contributions of organisational
structure to strategic decision-making and, in particular, whether an
organisation that has sub-units acting purely in their own interests can
1
http://fst.jpl.nasa.gov/FAQ.html
This sobriquet will be used to refer to the base simulation model for the remainder of
this document.
2
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outperform an organisation attempting to optimise performance across subunits.
Cognitive Capacity as a Source of Systematic Asymmetry
Schoemaker (1990) has argued strongly that “Asymmetry of any kind
constitutes a potential factor for rent creation” (p. 1187). However, it is the
underlying nature of an asymmetry that determines whether a factor will
actually be useful for rent creation or not. Asymmetries in firm performance
that arise due to luck or initial endowments are generally not considered useful
to strategists because they cannot be created, manipulated or controlled by
managers (Barney, 1986a).
Thus, the fact that oil deposits are scattered asymmetrically over (or just
under) the earth‟s surface, and that Saudi Arabia is lucky enough to have large
endowments of this valuable commodity, is of little interest to strategists.
However, in the oil exploration business, the quality of geological surveys is
strongly correlated with financial performance. Good quality geological
information reduces the number of expensive drilling operations and increases
the hit rate of drills that are sunk. Asymmetric performance occurs because
there is an asymmetric distribution of geological information, and this result
3
presumably arises from an asymmetrical distribution of skilled geologists .
Following Schoemaker (1990), we use the term „systematic asymmetry‟ to refer
to those cases where variations in a factor can be shown to be causally
associated with asymmetrical firm performance. These cases can be
distinguished from those involving luck, randomness or „unsystematic‟
asymmetry.
SELESTE is an ideal environment for studying the determinants of
systematic asymmetry. With SELESTE, it is possible to purposefully create
differences between agents and then conduct controlled experimental studies to
3
Pareto rent-seeking behaviour (see Chapter 2) may explain why good geologists are
attracted to good companies.
161
see whether these differences yield sustainable performance differentials over a
wide range of environmental conditions (aka strategic landscapes). The results
of these experiments may demonstrate that certain a priori differences can
yield absolute performance advantages, or alternatively, that they can alter the
probability that one competitor will outperform another.
Using a simulation model to demonstrate that a given factor can create
systematic heterogeneity in firm performance has several ramifications. First, it
may serve as a partial test of a theory; providing support for a particular
explanation of events over alternative explanations. Secondly, the simulation
results may serve as the basis for novel theoretical development based on a
clearer picture of the interaction between the respective factors in the analysis.
Finally, the insights gained from simulation experiments have the potential to
inform practice by highlighting the efficacy of a particular factor in rent
creation. The results may help to generate a greater awareness and improved
clarity of thought about the role and limitations of certain factors that managers
can manipulate to improve firm performance.
Cognitive Capacity as a Source of Systematic Asymmetry
The study of cognitive processes in strategy has traditionally focused on
limits to rationality in the strategic planning process. Important concepts, such
as bounded rationality, mental maps, framing, and dominant logic, have
emerged from this stream of research (Huff, 1990; Prahalad & Bettis, 1986;
Simon, 1979; Tversky & Kahneman, 1981). The major application of this
research has been to provide a range of explanations for strategic errors and
mistakes. For instance, a failure by a corporation to exploit a major opportunity
can often be attributed to a flawed mental map of the environment held by the
CEO (Calori, Johnson, & Sarnin, 1994).
More recently, several authors have begun to call for investigations of the
normative implications of cognition (Ginsberg, 1990; Ginsberg, 1994;
Schoemaker, 1990). Central to these arguments is the view that (ir)rationality is
162
not uniformly distributed among managers. According to Schoemaker (1990),
it is more reasonable to expect variable rationality in the marketplace. To admit
variable rationality opens the possibility that rationality can be systematically
controlled and hence form a basis for rent creation. Ginsberg (1990) has
formalised this argument by linking it to the resource-based theory of the firm.
For Ginsberg, socio-cognitive capabilities represent a scarce resource that
cannot be easily imitated. Moreover, possession of these scarce socio-cognitive
capabilities is valuable because they allow members of the firm, and in
particular senior managers, to make better quality decisions about the
deployment of extant resources.
The challenge of this view is to actually demonstrate that systematic
differences in rationality lead to changes in performance. Rationality is a
difficult construct to operationalise and measure. This study has focused on
cognitive capacity as one facet of rationality. Cognitive capacity is defined as
the capacity to “...register, store, use and make sense of data” (Gronhaug &
Kvitastein, 1992).
Several groups of strategy researchers have touched on issues related to
the cognitive capacity of a firm. For instance, Prahalad and Bettis (1986) have
argued that the size of a diversified firm is limited by the ability of the top
management team to manage strategic variety, where strategic variety refers to
“...the differences in strategic characteristics of the businesses in the portfolio
of the firm” (Prahalad & Bettis, 1986, p.490). According to this view, the
complexity of the top management process is a function of strategic variety.
Similar businesses can be managed in the same way, that is, with a single
dominant general management logic. Increasing strategic variety requires the
addition of new dominant logics or ways of managing, especially among the
top management team, who are usually responsible for the allocation of
resources among business units. Prahalad and Bettis (1986) view the inability
of the top management team to assimilate new dominant logics as a major
limiting factor in diversification. As a corollary, a top management team that
163
attempts to stretch a single dominant logic over a range of unrelated businesses
will perform poorly relative to a more tightly focused team.
A relationship therefore exists between cognitive capacity and the
number of dominant logics possessed by a firm. An increase in strategic variety
requires both an increase in cognitive capacity and an increase in the number of
dominant logics. However, whereas cognitive capacity implies some
quantitative measure of the sense-making ability of a firm, the dominant logic
viewpoint reminds us that qualitative changes in management outlook must
also occur in order to respond appropriately to complexity.
In contrast, Miller and his co-workers (Miller, 1990; Miller, 1993; Miller
& Chen, 1996; Miller, Lant, Milliken, & Korn, 1996) have studied the problem
of cognitive capacity from the perspective of strategic simplicity. Miller found
merit in the view that organisations should “stick to the knitting” and remain
relatively simple in their operations. However, he argued that successful
organisations often had a tendency to over-focus on activities in which they
were successful. Coming “...to focus more narrowly on a single theme, activity
or interest at the expense of all others” (Miller, 1993, p. 116).
According to this view, simplicity breeds success, but Miller argues that
success also breeds simplicity. “Left to their own devices, [successful]
organizations...tend to simplify their strategic repertoires and pursue
increasingly focused strategies.” (Miller et al., 1996, p. 864). In turn, this
oversimplification makes these organisations vulnerable to changes in the
environment. IBM‟s early success in mainframes led it to focus the company‟s
resources around the mainframe business as a key driver of profitability. The
emergence of the microcomputer in the mid-to-late 70s was dismissed by IBM
as a sideshow to the „main game‟ of mainframes. This allowed backyard
companies like Apple to obtain an early lead in the microcomputer market and
placed IBM in a position from which it has arguably never recovered.
164
Strategic simplification has the benefit of bringing parts of an
organisation together under a single powerful mission. Paradoxically, this leads
to escalating simplification where the organisation learns to rely on a single
recipe for success. Eventually, the „rules of the game‟ change and the
organisation invariably suffers from their lack of diversity in outlook. Miller
(1990) has labelled this phenomenon the “Icarus Paradox” after the figure in
Greek mythology who flew too close to the sun and plummeted to his death
after his wax wings melted. Similarly, organisations can bring about their own
downfall with an excessive reliance on the strategies that brought them past
success.
Both Prahalad and Bettis (1986) and Miller (1990) have advocated an
increase in cognitive capacity as strategic variety increases. They also find
virtue in simplicity, or in maintaining a single dominant logic, until conditions
dictate otherwise. Miller, in particular, views too much cognitive capacity as a
weakness, and a potential contributor to „paralysis by analysis‟. Clearly, finding
a middle ground between oversimplification and overcogitation is required to
find a resolution to this apparent dilemma.
Calori, Johnson and Sarnin (1994) have named this ideal state requisite
cognitive complexity. The concept owes much to Ashby‟s (1956) „law of
requisite variety‟. Ashby‟s law postulates that only variety can destroy variety.
An increase in environmental variety must be matched by an increase in the
variety of the system operating in that environment if a given set of outcomes is
to be maintained.
Consider a species of frog that lives on a diet of nocturnal flies. These are
the only flies in the pond that venture forth at night. In order to catch these
night flies, each frog's sensory apparatus needs to distinguish between night and
day, and between flies and non-flies. This level of variety in the system (frog)
is sufficient to achieve the required outcome (of eating flies) given the
complexity of the environment (the pond). If a new species of non-edible night
fly migrates into the pond then a frog must learn to discriminate between the
165
two species of night flies if it is to maintain its accustomed dietary intake. In
other words, an increase in the variety (complexity) of the environment results
in an increase in the level of variety within the system in order to maintain a
given outcome. The ability to distinguish between the two species of night flies
before the arrival of the second species would not have markedly improved the
earlier outcome, and would therefore constitute redundant information.
Thus, the relationship between cognitive complexity and performance
must be mediated by the complexity of the environment. All parties agree that
too little cognitive complexity at a given level of environmental complexity
will lead to sub-optimal performance. In addition, Miller (1990) has suggested
that too much cognitive complexity is also dysfunctional, while Ashby (1956)
views additional cognitive complexity as redundant information. Miller's theory
suggests an inverted-U shape relationship between cognitive complexity and
performance at a given level of environmental complexity, while Ashby's
theory would see performance plateau rather than decline once cognitive
complexity had reached an optimum level (see Figure 6.1).
Ashby
M iller
Performance
C ognitive C apacity
Figure 6.20 Two hypothesised relationships between cognitive
complexity and performance
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Method: Operationalising Constructs with SELESTE
The SELESTE simulation testbed was used to test the general theory that
increasing cognitive capacity increases performance as the environment
becomes more complex. The model was also used to test the secondary
hypotheses that too much cognitive capacity is either a) detrimental to
performance or b) is redundant and thus has no effect.
At the start of a simulation run, all firms (or agents) in the SELESTE
model started in the same position. Each firm started the simulation tabula
rasa, with a knowledge base that had all n rules set to zero. With experience,
firms learned about appropriate combinations of industry membership and asset
ownership that increased and maintained positive profits. These lessons were
stored in the firm‟s knowledge base. From the first round of the simulation,
firms began to have different experiences, and thus diverged in the content of
their knowledge base. This created asymmetry. Firms began the simulation in a
homogenous state and ended up in a heterogeneous state.
We were interested in whether systematic asymmetries in firm
performance could be created from these divergent experiences. In the current
study, cognitive capacity was hypothesised to vary systematically with
performance. Accordingly, SELESTE‟s firms/agents were varied to study the
systematic effects of cognitive capacity on performance. Specifically, the size
of a firm's rulebase was used as a simple proxy for cognitive capacity.
Equating cognitive capacity with the size of a firm's rulebase
corresponded well with Gronhaug's (1992) definition of cognitive capacity as
the capacity to “...register, store, use and make sense of data”. As we have
discussed in Chapter 4, the learning classifier system underlying the SELESTE
model explicitly relies on a rulebase to register, store and use data based on
feedback from the relationship between conditions and actions. A larger
rulebase can potentially classify and store a larger amount of information about
the environment and react more appropriately to various contingencies.
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Environmental complexity can also be well-defined in the SELESTE
environment. Firms in the simulation are able to detect the industry
membership status and asset ownership status of each firm in the simulation.
Both industry membership and asset ownership status are binary constructs.
Therefore, given nFirms number of firms in the simulation, nInds number of
industries, and nSAs number of strategic assets, the total number of
combinations or variety in the environment (VE) is given by the formula:
As we have seen in Chapter 4, when two firms compete in one industry
with a choice of two strategic assets, 64 possible states of the world can be
generated. Even a relatively small game with five firms, two industries and
three strategic assets generates a mammoth 33.5 million different states of the
world.
Design
Four environmental scenarios were created for the experiment. Two
scenarios were defined as low complexity having 64 states. Two scenarios were
defined as (relatively) high complexity having 256 states. The exact
composition of each scenario is detailed in Table 6.1.
Table 6.6 Environmental Scenarios
Complexity
Low
Low
High
High
States
64
64
256
256
nFirms
2
2
2
2
nInds
2
1
1
2
nSAs
1
2
3
2
Cognitive capacity, the second independent variable, was operationalised
as the size of a firm‟s rulebase. Five levels of rules were used in the
experiment: 16, 32, 64, 128 and 256. Half the rules in each run were industry
rules, half were resource rules (see Chapter 4). For each scenario, a full
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factorial experimental design enabled every combination of rules between the
two firms to be tested (eg. 16-16, 16-32, 16-64...256-256). Twenty-five
different combinations of rules (5x5) existed for each scenario. Therefore, for
four scenarios, 100 (4x25) unique runs were needed to test every combinative
level in the design. To enable more robust and reliable estimates of
experimental effects, a total of 1000 runs were undertaken, providing 10 data
points for each combination of the factors.
The cumulative performance of each firm was captured at the 500th and
5000th round in each simulation run. This enabled the performance differential
between each firm to be calculated. Capturing performance at t=500 and
t=5000 enabled us to measure whether early success was maintained through
the simulation or whether Miller‟s (1990) Icarus paradox applied and early
success led to subsequent failure.
Procedure
This section lists additional details of the simulation‟s implementation in
order to facilitate replication of the study. Familiarity with the SELESTE model
as presented in Chapters 4 and 5 is assumed. A summary of the parameter
values used in the model can be found in Table 6.2.
The experiment consisted of 1000 runs with 5000 iterations or rounds per
run. The runs were completed according to the schedule outlined in the design
section above. At the start of each simulation run, all rules in the firm‟s
rulebase were set to zero and the firm‟s initial starting position was set to zero
industries (A=0) and zero resources (D=0). Conversely, the other two matrices
were initialised to 1 (ie. B=1 and C=1). Thus, every strategic industry factor
was found in every industry, and every strategic asset matched every strategic
industry factor. The initial matrix conditions are identical to those used in
section 4.5.
Initial changes in the firm‟s rulebase occurred because of mutations. The
mutation rate was set at a declining rate according to the formula
169
0.5/log(currenttime). A declining mutation rate has the benefit of locking in
optimal strategies late in the simulation (Goldberg, 1989). The genetic
operators, crossover and mutation, were set to operate every 50 rounds. Thus,
the probability of mutation in the first operation was 0.5/log(50) or 30%. At
t=1000, the mutation rate fell to 5%, and by the end of the simulation (t=5000)
the rate was down to 1%.
In the crossover function, the lower quartile of rules (based on rule
strength) was replaced by new rules on each operation of the procedure.
Roulette wheel selection was used to select pairs of rules for the crossover
procedure. Scaling of fitness values occurred in order to prevent the roulette
wheel procedure being biased by highly successful rules (Goldberg, 1989).
Fitness values were re-scaled according to the formula Fnew=log(10Fold).
Table 6.7 Parameter Values
Parameter
Mutation rate
Rescale, New fitness
Taxation rate
GA Period
Asset charge
Growth Reward
Profit Reward
Noise in Bid
Value
=0.5/log(currenttime)
=log(10*old fitness)
0
every 50 rounds
0.1
10
1
mean=0
std dev=0.4*bid
Results
The research design called for the model to be run 1000 times. A set of
descriptive statistics describing the performance differential between the two
firms under conditions of high and low complexity is presented in Table 6.3.
As expected in a full factorial design, the mean values were not significantly
170
different from zero. A visual inspection of the performance data for each firm
revealed a skewed distribution with a long tail of positive values (see Figure
6.2).
Figure 6.21 Distribution of Performance (with box plot)
Table 6.8 Descriptive Statistics for Performance Differentials
Complexity
No. of Cases
Mean
Standard deviation
Maximum
Minimum
Low
500
-49.7
1338.3
1882.8
-1836.7
171
High
500
-172.2
1140.5
1854.6
-1781.8
Total
1000
-111.0
1244.2
1882.8
-1836.7
The primary hypothesis suggested that firms with more rules should
perform better than firms with fewer rules, and that this relationship should be
mediated by the complexity of the environment. A correlational analysis was
conducted to explore the relationships between the variables of interest. A
nonparametric technique was used to counter the skewness in the data set. The
results of this analysis are contained in Table 6.4. In general, the primary
hypothesis was not supported. Little correlation existed between the key
variables.
Table 6.9 Spearman Correlational Analysis
Variables
Perf. Differential
Rule Difference
Complexity
Perf. Differential
1
-0.06
-0.04
Rule Difference
-0.06
1
0.00
Complexity
-0.04
0.00
1
Although the data were highly skewed, a multiple regression analysis was
also used to test the primary hypothesis. The regression equation took the form:
It was anticipated that performance differences would be positively
associated with rule differences and positively associated with the interaction
between complexity and rule difference. In reality, the association between the
variables was exceedingly weak, with an r-squared value of 0.01. An analysis
of variance was also not significant (F(3,996)=2.55, p>0.05). At the level of the
individual parameters, it was not possible to reject the null hypothesis that the
parameter values were equal to zero (see Table 6.5). Thus, the primary
hypothesis found little support.
Table 6.10 Parameter Estimates
Parameter
Intercept
Rule Difference
Complexity
Interaction term
Estimate
-8.90
0.75
-0.64
-0.01
Std Error
76.30
1.24
0.41
0.01
172
t-ratio
-0.12
0.61
-1.56
-1.65
Miller‟s assertion that early success often led to failure was also tested. If
this were true, one would expect to find a negative correlation between
performance at t=500 and t=5000. In fact, the results showed that performance
was highly correlated at each stage (Spearman r=0.97 and r=0.94 for firms 1
and 2 respectively). Thus, in contrast with Miller‟s theory, firms that enjoyed
early success tended to continue to be successful. However, the absence of
turbulence in the simulated environment meant that firms did not have to alter
successful strategies over the life of the simulation. Environmental turbulence,
rather than the simple passage of time, may be a prerequisite condition for the
emergence of the „Icarus paradox‟.
An Alternative Approach
The disappointing nature of the results in the previous section prompted
us to explore an alternative experimental approach. This approach provided a
simpler, more direct test of the hypothesis that cognitive capacity affects
performance. It was felt that the complexity of the previous design may have
masked the predicted effects. Rather than varying the environment, it was
decided to select a fixed environment and then systematically vary the number
of rules assigned to each firm. Any change in performance differentials could
then be directly linked to the underlying rule changes.
Method
The strategic landscape consisted of four firms, four industries, four
strategic industry factors, and four strategic assets. The existence of four
industries and four strategic assets gave each firm 256 possible strategic
choices. Two firms were designated as having a low cognitive capacity, while
the remaining two firms were classified as high cognitive capacity.
As in the previous study, cognitive capacity was operationalised as the
number of rules in a firm‟s rulebase. High capacity firms had exactly double
the number of rules of low capacity firms. Each low capacity firms started with
173
one rule, and this was systematically increased by one rule each time the
simulation was run to a maximum of 256 rules. Thus, high capacity firms
started with 2 rules and finished with 512 rules.
The learning classifier system from the previous study was replaced with
a more primitive adaptive algorithm. The new algorithm utilised only rule
activation, feedback and reward sub-routines, and lacked the if-then structure
of the learning classifier system. Rules consisted solely of valid 8-bit strings of
industry-membership and asset-ownership data (eg. 1001-1001). At the start of
each simulation run, the rules for each firm were drawn (with replacement)
4
from the set of 256 possible actions . Each rule was given an arbitrary strength
of 5 units. Each round of the simulation, a firm selected one rule from its
rulebase to activate. Two rule selection schemes were used - roulette wheel and
noisy bidding. Under the roulette wheel selection regime, the probability of a
rule being selected was proportional to its strength (Goldberg, 1989). Stronger
rules had a proportionally higher probability of selection. In the noisy bidding
selection scheme, each rule bid to be activated, with the value of the bid equal
to the strength of the rule plus an error term (Bruderer, 1993). The value of the
error term was drawn from a normal distribution with mean of zero and
standard deviation equal to 0.3 times the rule strength. The rule with the
highest bid was activated.
The active rule‟s strength was modified on the basis of the profit result
obtained in the round in which it was active. A rule that yielded a negative
profit had its strength automatically reduced to one. Rules generating a positive
profit had their strength increased by 10 units. If the profit improved from the
previous round, the rule received an additional 10 points of strength. Thus,
successful rules quickly became differentiated from unsuccessful rules. Finally,
there were no crossover or mutation routines to generate new or fitter rules firms could only select from their initial rulebase.
4
four strategic assets, four industries each represented as binary digits = 2^8 or 256
combinations
174
Design
A total of 256 runs were needed to cycle through the number of rules
possessed by low capacity firms, nLoRules{1,2,3,..., 256}. The entire cycle
was repeated ten times (for a total of 2560 runs) under each activation regime
to obtain a reasonable sample of points at each level. In each run, the
simulation was run for an arbitrarily long period of time (2560 iterations) to
enable clear results to emerge. At the end of each simulation the cumulative
profit performances of the low capacity and high capacity firms were retained.
This enabled the performance differential between the two groups to be
calculated and the resulting value was used as the major dependent variable in
the study.
Results
The results in Figure 6.3 depict the mean performance difference between
the high and low groups under the two selection regimes. The data have been
grouped into 16 groups of 160 data points to facilitate interpretation. In both
cases, the high capacity firms clearly do best when the number of rules is
relatively low.
Figure 6.22 Results of Second Cognitive Capacity Experiment
175
Linear and quadratic approximations were fitted to each scatterplot. Table
6.6 contains the results of these analyses.
Table 6.11 Results of Curve Fitting
A. Roulette Wheel Selection
Analysis
Rsquare
Intercept
x
x^2
F ratio
Linear
0.73
572.3*
-64.5*
na
37.63*
Quadratic
0.84
889.9*
-170.3*
6.23*
34.92*
Linear
0.43
940.2*
-49.4*
na
10.40*
Quadratic
0.61
540.7
83.7
-7.8
9.99*
* - p<0.01
B. Bid Selection
Analysis
Rsquare
Intercept
x
x^2
F ratio
* - p<0.01
In both cases, the quadratic fit yielded a higher R-squared value than the
linear fit. Interestingly, both cases lend support to Miller‟s (1990) conjecture
that too much cognitive complexity can lead to poor performance. However,
the data from the bid selection regime more closely matches our prior
expectations, with performance initially rising with cognitive complexity and
then falling. In contrast, the data from the roulette wheel selection process had
performance falling after the first group of rules and then approaching an
asymptote after 128 rules. In both cases, the linear trend was downward sloping
indicating that the performance differential between firms with high and low
cognitive capacities tended to fall as the absolute number of rules increased.
However, as the number of rules increased in the roulette wheel condition, the
hi-rule firms went from being positive to negative performers (ie. having more
176
rules went from being a competitive advantage to a competitive disadvantage).
In the bid selection condition, higher numbers of rules reduced the performance
differential between the two types of firms to zero (ie. no competitive
advantage).
Discussion
The results of these experiments reveal just how sensitive results in
simulation studies can be to the specific algorithmic implementation of a
model. The first experiment suggested that cognitive capacity did not play any
role in the creation of competitive advantage (or disadvantage). On the other
hand, with relatively minor changes to the model, the second study revealed
that differences in cognitive capacity yielded a competitive advantage when
firms had small absolute numbers of rules. The roulette-wheel and noisy bid
selection regimes both suggested that the extent of this competitive advantage
declined as the absolute numbers of rules increased. However, under a roulette
wheel selection regime having more rules led to a competitive disadvantage,
whereas the performance differential declined to zero under the noisy bid
selection regime. The ambiguity present in the results raises obvious questions
about how the results should be interpreted and the external validity of the
study.
Explaining the difference between the noisy bid and roulette wheel results is
relatively straightforward. As a simulation progresses, the roulette-wheel
selection regime places greater and greater weight on successful rules (see Figure
6.4). However, there is still a finite probability that an unsuccessful rule will be
selected as even unsuccessful rules have a strength of 1 unit. As the size of the
rulebase increases, so do the number of unsuccessful rules.
177
Figure 6.23 Example of roulette wheel selection in operation
The probability of selecting an unsuccessful rule with a large rulebase is
higher than with a smaller rulebase as the absolute number of poor rules in the
rulebase is greater. This may explain why firms with a larger rulebase
experience poorer performance under the roulette-wheel selection regime.
The noisy bid approach has a much lower chance of selecting poor rules.
As the simulation progresses, successful rules move further and further away
from unsuccessful rules even though the size of the error term grows (see
Figure 6.5). The probability of selecting an unsuccessful rule towards the end
of a simulation run with the noisy bid condition is extremely low - much lower
than in the roulette wheel condition. Therefore, high cognitive capacity firms
using the noisy bid selection regime do not suffer the poor performance effect
observed in the roulette wheel results. However, this does not satisfactorily
explain why the performance differential in the noisy bid condition trends from
a high positive value towards zero as the absolute number of rules increases.
One explanation is that as the absolute number of rules grows, the firms
with lower cognitive capacity have a greater range of rules from which to select
thus the probability of possessing a rule that yields a good competitive outcome
(ie. a
178
Figure 6.24 Example of noisy bid regime in operation
positive performance given the actions of other players) rises. At very high rule
levels, the diversity (or range) of rules held by high and low cognitive capacity
firms are virtually indistinguishable and thus no competitive advantage accrues
to the high cognitive capacity firms for holding rules that low cognitive
capacity firms cannot match. This is reflected in a steady decline in the
performance differential as the absolute number of rules increases. At the
extreme, the performance differential approaches zero, reflecting an ability for
low capacity firms to find an adequate rule despite only having half as many
rules to select from. The secret seems to lie in having an adequate diversity of
rules rather than a sheer volume of rules.
Note that the same principle also applies to the high cognitive capacity
firms. These firms experience an increasing performance differential over low
capacity firms as the size of their rulebase increases and hence their ability to
select from a wider range of strategies to find a profitable course of action.
Then, the performance gap closes once again as the returns to rule diversity
diminish and low capacity firms develop increasing rule diversity of their own
(see Figure 6.3).
179
Nevertheless, the results of the first experiment are difficult to explain.
The learning classifier systems seem to be operating well because firm profits
were positive (mean performances were 598 for Firm 1 and 709 for Firm 2).
We have already commented on the skew in the data towards positive
performances. Strangely, the correlation between the performance of Firm 1
and Firm 2 was large (r= -0.89, n=1000, p<0.001). High performance by one
firm implied poor performance by the other. It was expected that differences in
the number of rules would account for the performance differential and yet no
correlation between rules and performance was found.
In the absence of additional data, we are unable to draw firm conclusions
about why this pattern of results emerged. One hypothesis is that the results
were due to premature convergence in the rulebase (Goldberg, 1989). If by
chance, a firm locks onto a strategy that markedly increases its performance
then the rule generating that strategy will be rewarded well. After a few rounds
of good performance the rule will come to dominate the selection and
reproduction sub-systems; winning all bidding contests and being selected as a
parent rule in every crossover. In effect, the rulebase starts to act as if it only
has one rule. The performance of the firm would be independent of the number
of rules but the firm would still perform well.
There is one flaw in this line of argument. Normally, possessing a larger
number of rules should give the firm more opportunities to search the problem
space. Having more rules should theoretically increase the chance of the firm
stumbling on a performance-enhancing strategy in the first place because the
mutation process is able to operate on more elements. More rules to mutate
implies more variation and thus better strategies. However, it could be possible
that the combination of premature convergence with a small problem space has
produced the unexpected result in our experiment. If a small problem space
allows an optimal strategy to be found with little search activity then the
advantage of a larger rulebase would be nullified. It may be that learning
180
classifier systems may not be suited to problems that don‟t give them the
„room‟ to operate.
Conclusion
The primary experiment failed to find support for the hypotheses raised
in this study. This was probably due to methodological problems in the design
of the experiment. A second round of studies, which utilised a modified
methodology, found strong support for the notion that a higher cognitive
capacity was important when the number of rules being used was low but that
the benefits of higher cognitive capacity declined as the number of rules in use
increased.
It was argued that a diversity of strategies or „a strategy for all seasons‟
was the important underlying factor rather than cognitive capacity per se.
Increasing the size of the rulebase (for both high and low capacity firms)
increased the chances of finding a good strategy. However, the returns from
diversity diminished beyond a certain point, and could also be nullified by
competitors increasing the extent of their own rule diversity.
The notion that cognitive diversity plays a key role in the quality of
strategic decisions has considerable theoretical and empirical support. The
concept that several dominant logics may be required to run a large diversified
firm supports finding that diversity plays an important role in strategic
decision-making (Prahalad & Bettis, 1986). In addition, a wide range of
empirical studies in management and psychology have demonstrated the
importance of diversity in group decision making and the role cognitive
diversity plays in generating high quality solutions to problems (Amason, 1996;
Bantel & Jackson, 1989; Wanous & Youtz, 1986).
The results of the second experiment in this study support and extend
these results. We have demonstrated that there are diminishing returns to
diversity and that high levels of cognitive diversity may be unproductive if
competitors have sufficient diversity and flexibility to meet emerging strategic
181
challenges. Furthermore, the results of the first experiment suggest that a good
strategy that performs well may act as a substitute for cognitive diversity.
Peters and Waterman‟s (1982) exhortation to „stick to the knitting‟ may still
prove useful in simple environments.
Innovation and Imitation as Competitive Strategies
The preceding experiment investigated the effect of changing the
cognitive capacity of the firm. In contrast, this study seeks to explore the
environmental conditions under which innovation may prove to be a more
successful competitive strategy than imitation.
Introduction
There is no doubt that innovation, defined here as the ability of
individuals or organisations to create new products or processes that generate
economic value, is highly prized in Western business circles. Innovative
companies such as 3M, Kodak and Apple pervade our casebooks and business
magazines. Entrepreneurial figures like Henry Ford, Bill Gates and Rupert
Murdoch, stand out as role models and public icons.
Organisational theorists have been wary of this cult of innovation.
Population ecologists point to a „liability of newness‟ in their studies of
organisational failures and maintain that long-lived organisations are relatively
inert (Hannan & Freeman, 1977; Hannan & Freeman, 1989). Institutional
theorists argue that most organisations are risk averse, preferring to adopt
structures and routines that conform to social expectations about the „correct‟ or
„legitimate‟ way of organising. For institutional theorists the question is not why
organisations are so different, but rather, why they are so similar (DiMaggio &
Powell, 1983).
The successful rise of Asian economies, such as Japan, Korea and China,
over the past few decades has also led strategy researchers to question the
effectiveness of imitative strategies. Much of the success of these Asian
182
economies has been attributed to their ability to appropriate Western
technological innovations and engineer higher quality and/or lower cost
alternatives. The West‟s cultural bias against imitation can be discerned by
derogatory references to these Asian imitators as pirates, clones or copycats
(Bolton, 1993).
The majority of studies on innovation have studied intraorganisational
factors that are associated with innovative behaviour. Popular mediating
variables have included leadership style, organisational structure and reward
systems (Fiol, 1996). Researchers have relied almost exclusively on case
studies or large cross sectional studies to explore these relationships (Lant &
Mezias, 1990). Such studies have been criticised for having a survivor bias
because of their tendency to focus on the attributes of successful innovators
rather than the population of innovators as a whole (including failures). Few
studies have focused on the external factors favouring innovation as a
competitive strategy or the way that innovation creates competitive advantage
over time. The paucity of research in this area can be partly attributed to the
difficulties of assembling, classifying and interpreting large longitudinal data
sets on populations of organisations (Lant & Mezias, 1990).
Computer simulation is a useful tool to overcome the “limitations of the
natural laboratory” described above. Via simulation, researchers are able to
create multiple microworlds upon which to experiment (Brehmer & Dorner,
1993). “The researcher is able to control all the variables under consideration,
manipulate them to uncover their effects on dependent variables over time,
examine all possible combinations and interactions of variables, and examine
the dynamic effects of the variables” (Lant, 1994, 196). In addition, input,
process and output variables can be measured with a high degree of accuracy
and reliability. Thus, the messiness of the real world is replaced by a pristine
artificial world where the experimenter has a high degree of control over the
interactions that occur.
183
The Lant-Mezias Model
In the Lant-Mezias simulation model, organisations were characterised by
symbol structures of four binary symbols; resulting in 16 possible
organisational forms (e.g. 1011, 1000, 1100). Each of these organisational
forms was randomly assigned a base performance level ranging from -10 to
+10. Turbulence was simulated by reinitialising base performance levels on the
25th iteration of the model.
The goal of each organisation in this simulated world was to improve
performance by searching the 16 possible organisational forms. Three generic
search strategies were postulated. Fixed firms did not search or change their
initial position reflecting a philosophy of inertia (Hannan & Freeman, 1984),
while imitative firms searched for the largest organisation in the population
and adopted its characteristics. This follow-the-leader strategy was claimed to
be indicative of organisations seeking to legitimate their structure in an
institutional context (DiMaggio & Powell, 1983).
In the final search strategy, adaptive firms actively sought new
combinations by changing 1-4 symbols in the symbol structure each round; the
magnitude of the change being related to the discrepancy between aspired and
actual performance relative to other firms. Regardless of whether the change
occurred on 1 or 4 dimensions, organisations were always assumed to select the
best performance-enhancing change. In unambiguous conditions, base
performance levels were known with certainty. In ambiguous conditions, a
random error term ranging from -25 to +25 was added to the base performance
for each search, thus adding considerable noise to the search process. Changes
were also costly. Each dimension changed cost the organisation 10% of its total
resources.
Lant and Mezias reported the mean resources and performance for each
strategy in each round of the simulation. In the unambiguous condition, it was
observed that adaptive and imitative firms shared the same level of mean
184
resources over time and both exceeded the level of resources of fixed-strategy
firms by a significant margin. Around the 25th period, where turbulence was
introduced, adaptive firms reacted faster to the changes but were matched by
imitative firms within 15 rounds.
In the ambiguous condition, fixed and imitative firms outperformed
adaptive firms on the level of mean resources although adaptive firms
performed better on period-to-period performance. This high period-to-period
performance but lower overall resources (cumulative performance) for adaptive
firms can be explained by the increased costs of change incurred by adaptive
firms. Uncertainty about the best performing configurations led to errors and
hence more frequent and costly changes.
Limitations of the Lant-Mezias Model
Simulations are relatively rare in the organisation and management
literature and simulations that include explicit models of firm performance are
even rarer. The most common approach has been to model performance
exogenously, with results determined through various mechanisms, such as
lookup tables (Lant & Mezias, 1990), payoff functions (Nelson & Winter,
1982), or random walks (Levinthal, 1991; Levinthal & March, 1981; Mezias &
Glynn, 1993).
The use of exogenous performance routines has never been entirely
satisfactory from a strategic management perspective because no allowances were
made for competitive forces. In exogenous models, performance is determined
independently of the actions of other firms. The Lant-Mezias model used an
exogenous performance model to assign all firms with the same configuration the
same level of performance. In reality, as more competitors enter a given niche
individual firm performance will decline. In contrast, SELESTE utilises a
competitive performance model that lowers the performance of firms engaged in
similar strategies.
185
In the Lant-Mezias model, adaptive firms engage in random or blind
search for higher performing configurations. In reality, adaptation is not blind.
Firms will tend to concentrate search around past successes. In SELESTE,
firms intelligently build on past successes rather than engage in blind search.
Hypotheses
The current study attempted to reproduce three key results from Lant and
Mezias‟ (1990) study:
H1: Under unambiguous conditions, the cumulative performance of
adaptive and imitative firms should be equal, and significantly greater than the
performance of fixed firms.
H2: Under ambiguous conditions, the cumulative performance of
imitative and fixed firms should be equal, and significantly greater than that of
adaptive firms.
H3: In conditions of greater turbulence, the cumulative performance of
adaptive firms should increase.
Method
Design
A full factorial experimental design was created using two levels of
ambiguity, two levels of turbulence and two levels of imitability. Ten
observations were collected for each combination resulting in a total of 80 runs.
Each run was conducted for 200 periods, taking an average of 15 minutes per
run. Key parameters included 5 firms of each strategy type (fixed, imitative and
adaptive), 2 industries, 10 strategic industry factors, and 10 strategic assets. The
rulebase for each firm consisted of 20 rules; 10 rules examining industry
membership conditions and 10 rules examining asset ownership conditions. In
the Lant-Mezias study, firms went bankrupt when their resources fell below
186
zero. To obtain unbiased estimates of the cumulative effect of each strategy,
bankruptcy was not included in the replicated study.
Search Strategies
The learning classifier system used by firms in the SELESTE model was
retained as an operationalisation of Lant and Mezia‟s adaptive search strategy.
The SELESTE model was extended to create firms with fixed and imitative
strategies.
Fixed strategy firms did not update their rulebase after the first period.
Firms always started the simulation with zero assets in zero industries and all
rules advocating the status quo. This would have resulted in zero performance
each round for a fixed firm. However, fixed firms were allowed to “mutate” the
rulebase with a 5% chance of a symbol change in each rule. Thus, fixed firms
could change without adopting a conscious search strategy. However as the
results would later show, mutation and natural selection were potent forces
even in the absence of reproduction and crossover that was only found in
adaptive firms.
Imitative firms operated by acquiring the best rules of successful firms.
The weakest ten rules were replaced one at a time by: choosing a random firm
that made a net profit in the round; selecting a random rule from the strongest
50% of rules for that firm; only replacing the weak rule if the strength of the
new rule was greater.
Ambiguity , Imitability and Turbulence
Lant and Mezias (1990) modelled ambiguity as large variations around
base performance that confused search and change processes. SELESTE‟s
competitive performance routine makes it much more difficult for any
competitor to accurately determine performance in advance. Instead, ambiguity
refers to the noise surrounding the selection of the best rule in each round. In
unambiguous conditions, the level of noise is set to zero; in ambiguous
187
conditions the level of noise follows a Gaussian distribution with mean of zero
and standard deviation of 0.4 of rule strength.
In the initial testing of the extended model, the imitative search strategy
was found to be very powerful. The resource-based view has suggested that
strategic assets generating a competitive advantage should be difficult to
imitate (Barney, 1991). To simulate this proposition, two levels of imitability
were introduced. In the high imitability condition, imitative firms perfectly
acquired the rules of successful firms. In the low imitability condition,
imitative firms had only a 1% chance per rule of successfully acquiring the
target rule.
In the Lant-Mezias study, turbulence consisted of a once-off reordering of
base performance payoffs. In the SELESTE model, turbulence can be modelled
as changes to the industry structure (B) and strategic fit (C) matrices. In the low
turbulence condition, each symbol had a 0.1% chance of changing each round,
while in the high turbulence condition the probability rose tenfold to 1%.
Results
The cumulative performance of the five firms in each strategy type was
collected at the completion of each 200-round simulation run. The use of
cumulative performance allowed a summary of the total effect of a strategy
across the whole simulation rather than a point-estimate of performance at a
single point in time. The distribution of total cumulative performance across all
strategy types varied widely with a mean of 799.0 and standard deviation of
297.5 (see Figure 6.6). Data on cumulative performance was converted to an
ordinal scale to control for the wide variations in total performance from one
run to another (Ruefli, 1990). A rank of 1 was assigned to the strategy with the
highest cumulative performance down to a rank of 3 to the strategy with the
lowest cumulative performance.
188
0
200 400 600 800 1000
1400
Figure 6.25 Distribution of Total Cumulative Performance
An analysis of variance was performed on the four factors hypothesised
to influence rank performance. Overall, the four-factor model accounted for
almost 50% of the variation in performance (see Table 6.7).
Table 6.12 Analysis of Variance in Rank Performance
Source
S
SxA
SxI
SxAxI
SxT
SxAxT
SxIxT
SxIxAxT
Whole Model
Error
Total
DF
2
2
2
2
2
2
2
2
16
223
239
SS
0.93
0.68
69.53
2.58
0.98
0.33
1.58
1.23
77.8
82.2
160.0
F Ratio
1.25
0.92
94.31*
3.49*
1.32
0.44
2.14
1.66
13.19*
* - p<0.05
S=Strategy, A=Ambiguity, T=Turbulence, I=Imitability
However, contrary to expectations, ambiguity did not result in significant
performance differentials, nor did turbulence appear to play a major role. It was
the degree of imitability that proved to be the major factor in determining
variations in performance (see Figure 6.7). When imitability was high,
imitative firms outperformed other strategies. When imitability was low,
adaptive and fixed firms performed better than imitative firms. Surprisingly,
189
the random change experienced by fixed firms was equally effective as the
crossover strategy of adaptive firms in the low imitability condition.
Figure 6.26 Interaction of Strategy and Imitability
The only other significant result was a strategy-ambiguity-imitability
interaction (see Figure 6.8). In the high imitability condition, fixed firms
performed significantly worse under conditions of high ambiguity (t=-2.22,
p<0.05). In the low imitability condition, adaptive firms performed better under
conditions of high ambiguity (t=1.91, p<0.1).
Figure 6.27 Interaction of Strategy, Imitability and Ambiguity
190
Discussion
The current study fails to find support for any of the hypotheses drawn
from Lant and Mezias‟ (1990) original study. The first two hypotheses
predicted differential performance by strategies depending on the level of
ambiguity. The third hypothesis predicted a relationship between strategy and
turbulence. However, the ANOVA results indicated no significant differences
in the strategy-ambiguity or strategy-turbulence interactions. The level of
imitability was a much greater determinant of performance. In the high
imitability condition, imitative firms (370.4) substantially outperformed
adaptive (223.6) and fixed firms (178.8) on mean cumulative performance.
In the Lant-Mezias model, performance was determined exogenously and
imitative organisations could do no better than copy the form yielding the best
performance level; a state that was also easily found by the majority of adaptive
organisations. In the current study the search space was much larger and
performance occurred under a competitive regime. In the high imitability
condition, imitative firms flawlessly copied the rules of the best performers
across all search strategies (including other imitators), thus displaying a
superior learning ability in a complex environment.
The resource-based school has greatly emphasised the role of
inimitability in sustaining competitive advantage (Barney, 1991; LengnickHall, 1992; Lippman & Rumelt, 1982). The results of this study support the
resource-based view. When resources were hard to copy innovators
outperformed imitators. However, it should be noted that the probability of
imitation was set at a very low level (1%). Given slightly higher probabilities of
imitation (say 5%-10%) imitators would have still outperformed innovators.
This suggests that closer attention should be paid to why firms do not imitate
more. Imitation is still a successful strategy even if the probability of imitation
is rather low. Rumelt‟s recent treatise on the causes of organisational inertia
may provide some valuable directions in this area (Rumelt, 1995).
191
Also surprising was the success of random mutation as a mechanism for
change. While adaptive firms recombined successful rules into new rules, fixed
firms simply had symbols in existing rules randomly changed. On average,
random variation performed as well as crossover. Although at the population
level random variation appeared to work, further work is required to test
whether random mutation enhances the survival and performance of individual
firms.
Variations in results from simulation to simulation can usually be traced
to variations in the assumptions of the underlying models. For instance,
switching from an exogenous to endogenous performance model visibly
changed the performance characteristics of imitative firms. However, this
chronic sensitivity to assumptions that is a characteristic of simulation
approaches can often be outweighed by the ability of a simulation model to
generate new insights. For example, although the current model failed to
duplicate Lant and Mezias‟ original results, it achieved in highlighting the
effectiveness of imitation as a competitive strategy to a much greater extent
than the previous study. This ability to generate new insights reflects the
contribution that simulation methodology can make to theory development.
Conclusion
A strong belief in the power of innovation has been one of the defining
characteristics of Western business. The current study suggests that imitation,
rather than innovation, may be a superior competitive strategy from a
population-level perspective. While individual innovators may outperform the
imitative population, the average performance of a group of innovators tends to
lag imitators. Innovation is inherently risky. Our hearts may tell us to bet on the
risk-taker, but gambling on the imitator is a more successful strategy in the
scenario outlined here.
192
Organisational Patches
Stuart Kauffman, a resident scholar at the Santa Fe Institute for the study
of complexity, has discovered that as complexity increases in a certain class of
fitness landscape it becomes more effective to break the landscape into
„patches‟ and seek to selfishly optimise each patch rather than to seek a global
optimum (Kauffman, Macready, & Dickinson, 1994). In his latest book,
Kauffman (1995) speculates that organisations have learned to use
multidivisional structures to break their environment into „patches‟. By
implication, Kauffman is suggesting that senior managers in complex
organisations should allow their divisions to selfishly maximise performance
even at the expense of the performance of other divisions. In complex
situations, top management should not attempt to globally coordinate the
actions of the corporation. Interestingly, work by Hitt and Hoskisson at Texas
A&M University on the strategic control of multidivisional corporations
provides some support for Kauffman‟s hypothesis.
Kauffman‟s initial work was done using a model based on biological
processes. The goal of this experiment was to replicate Kauffman‟s original
findings using SELESTE, a computational model explicitly designed with the
capability to simulate a multidivisional organisation in a competitive
environment.
Background
One of the most enduring topics in the strategic management literature
has been the study of the relationship between strategy, structure and
performance. The work of Alfred Chandler (1962) stands out as one of the
seminal contributions in this area. According to Chandler (1962), the evolution
of the multidivisional (or M-form) structure in the 1920s was a strategic
response to the increased complexity of managing early diversified companies
such as Du Pont and General Motors. Organising discrete businesses into
divisions freed top management from day-to-day operating decisions and
193
enabled them to concentrate on the overall strategic direction of the
corporation. The multidivisional structure also enabled top management to
identify and compare the performance of each business; significantly improving
the resource allocation process. Consequently, by the 1960s, the M-form had
become the most prevalent form of organisation among large diversified firms
(Rumelt, 1974).
Researchers have recognised that not all M-form corporations are
managed in the same way. At least two distinct styles are apparent (Goold &
Campbell, 1987; Hoskisson, Hill, & Kim, 1993). Strategic control “...refers to
the ability of top-level managers to use strategically relevant criteria when
evaluating plans and competitive intentions proposed by business unit
managers” (Hitt, Hoskisson, & Ireland, 1990, p. 33). Managers using strategic
controls explicitly seek to coordinate the efforts of divisions under their control
by developing economies of scope, synergy and resource sharing. As such, the
control system requires top-level managers to have a good intuitive
understanding of the constituent businesses.
Financial control refers to the use of objective performance criteria, such
as return on investment (ROI), to evaluate the performance of business unit
managers. In this system, divisions are treated as independent businesses that
are required to reach or surpass corporate-level financial targets. Unlike
strategic controls, financial control systems can be operated with little or no
operational knowledge of the underlying businesses (Hoskisson & Hitt, 1988).
Resource allocation is competitive, and the firm essentially operates as an
internal capital market. Williamson (1985) has argued that the superior
information and control available to top-level managers vis-a-vis the external
capital market makes investment in an M-form corporation more efficient than
an equivalent investment in a series of stand-alone businesses.
Goold and Campbell (1987) using a sample of UK corporations, found
that firms using financial control systems outperformed strategic control firms
by half a percentage point of return on investment over a five year period
194
(although they felt that strategic control may have been equally effective over a
longer time period). However, companies that attempted to mix strategic and
financial controls significantly underperformed relative to the pure styles.
Goold and Campbell‟s (1987) work implies that top-level managers have a
choice in selecting control styles and that the observed choice of style simply
reflects some preference on the part of the top management team. Later work
by Hitt, Hoskisson and others has suggested that the level of choice in control
systems may in fact be more limited.
According to Hitt, Hoskisson and Ireland (1990), the choice of control
system in an M-form corporation is a function of the degree of diversification
and the size of the firm. Larger, more diversified firms have to rely more on
financial control systems because they enable complex business information to
be reduced to a small set of objective financial measures. The use of these
metrics facilitates quick and easy performance comparisons between business
units.
The corollary of this statement is that top-level managers simply do not
have the time, or in-depth knowledge, required to undertake strategic control of
a large number of unrelated businesses. The larger the corporation, the greater
the volume of information that must be considered, and the greater the reliance
on the financial control system to summarise complexity. In addition, top-level
managers in highly diversified firms face a double jeopardy. Not only is the
quantity of information high in these type of firms, but the strategic situation in
each division differs markedly. For instance, the knowledge required to run a
steel business is different to the knowledge required to run a mining operation.
A top-level manager raised in the steel business will have trouble
understanding the complexities of the company‟s mining operation. Large scale
diversification may thus result in a shift in the centre of gravity of a firm, taking
it outside its original core business, and thus outside the strategic knowledge
195
5
base of its senior management (Hoskisson & Hitt, 1988) . Financial control
systems thus mask the need for strategic knowledge about each business in the
firm‟s portfolio; thus reducing information complexity and allowing a greater
span of control.
However, Hoskisson and Hitt (1988; 1994) have argued that this reliance
on financial controls leads to certain pathological behaviours in diversified
organisations. Given that the salary and promotion prospects of divisional
managers will tend to be linked solely to financial performance (usually return
on investment), an incentive exists for these managers to manipulate the
performance measurement system. Hoskisson and Hitt (1994) argue that the
easiest way to improve return on investment is to reduce long term investment.
Several studies have demonstrated that investment in research and development
in diversified firms is systematically lower than stand-alone businesses. There
is also evidence that divisional managers in financial control firms are more
risk-averse, and engage in less innovation and less interdivisional cooperation
than managers in other structural arrangements. Hoskisson and Hitt (1994)
view this behaviour as detrimental to the long term performance of a
6
corporation and urge top-level managers to „downscope‟ (and downsize) to
regain strategic control of their business.
There has been an underlying theme running through the strategic
management literature on diversification that suggests that detrimental effects
occur when top-level managers grow a corporation beyond the point where it
can be effectively administered. The notion that management capacity acts as a
limit on the growth of a firm can be traced back to the writings of Penrose
(Mahoney & Pandian, 1992; Penrose, 1959). More recent expositions on this
theme have argued that it is the cognitive limitations of the top management
team that sets the maximum extent of a firm‟s scope (Ginsberg, 1990; Prahalad
& Bettis, 1986). Hoskisson and Hitt (1994) have taken this analysis another
5
a sentiment echoed by Prahalad and Bettis (1986) with their concept of a „dominant
logic‟
6
that is, reduce the number of divisions to a single dominant logic or centre of gravity
196
step by arguing that top-level managers compensate for their lack of cognitive
capacity by changing the management control system within the firm to reduce
complexity. However, they argue that this change has serious implications for
the long-term performance of the firm.
Kauffman‟s hypothesis that local control is superior to global control at
high levels of complexity is therefore somewhat surprising given the antipathy
of strategic management writers to a loss of control by top-level managers.
Although Hoskisson and Hitt (1994) predict that increasing complexity is likely
to lead to the implementation of financial (local) controls over strategic
(global) controls, they see this move as ultimately detrimental to the
performance of the corporation. Kauffman, on the other hand, finds that higher
levels of financial control improve performance at higher levels of complexity.
The next section discusses how Kauffman and his colleagues managed to reach
this conclusion.
Kauffman’s NK Model
The dominant research paradigm in complexity science is the study of
artificial adaptive agents on a fitness landscape (Waldrop, 1992). This implies
both a specification of a fitness landscape and the design of an adaptive
algorithm. SELESTE uses learning classifier systems to enable agents to adapt
to their environment. The fitness landscape is created through the action of
competitors, industry factors and strategic assets operating in an environment
chosen to represent fundamental properties and interactions of interest to
strategy researchers.
For the „patches‟ experiment, Kauffman et al (1994) used an NK model
to create a fitness landscape. An NK model is an abstract model derived from
„spin glass‟ studies in physics where the magnetic field (or energy) created by
magnetic dipoles spinning in similar or opposite directions are the object of
interest (Kauffman, 1992). Note that the original motivation for the NK model
does not lie in business or economics.
197
The NK model can be conceptualised as a two dimensional nxn lattice of
N sites or agents. Each agent can exist in A states or spins. In the „patches‟
study, A=2 so that each site could be represented by a binary digit (ie. either „1‟
or „0‟). Each state also generated an energy level, E, that was determined by the
state of the agent and the states of K other sites coupled to the agent. At the
start of each simulation run, a lookup table was generated for every possible
combination of states influencing the energy level of a given site (see Table
6.8). The energy level for each combination was taken as a random real number
between 0.0 and 1.0. The number of possible energy combinations needed for a
given site was calculated as . In a spin glass world, lower energy states are
more desirable. Accordingly, the objective function of the exercise was to
minimise the average energy level across the N sites in the lattice.
Table 6.13 Example of NK model payoffs, N=3 K=2
States
s1
0
0
0
0
1
1
1
1
s2
0
0
1
1
0
0
1
1
Source: (Kauffman
s3
0
1
0
1
0
1
0
1
Energies
E1
0.6
0.1
0.4
0.3
0.9
0.7
0.6
0.7
E2
0.3
0.5
0.8
0.5
0.9
0.2
0.7
0.9
E3
0.5
0.9
0.1
0.8
0.7
0.3
0.6
0.5
Mean E
0.47
0.50
0.43
0.53
0.83
0.40
0.63
0.70
et al., 1994)
A fitness landscape is created by the NK model because the coupling
together of multiple sites means that minimising the energy for one site
inevitably raises the energy level at one or more other sites. Clearly, the more
198
couplings, the more complex the optimisation task, and the more rugged the
fitness landscape. Thus, the parameter, K, is used as a proxy for the degree of
complexity in a model. Put simply, as the level of K increases for a given N the
optimisation problem becomes much harder. Even in Table 6.8 it is not obvious
that the triplet {1,0,1} is the global minima because it generates one of the
highest energy levels for site 1.
The NK model used in the „patches‟ experiment used a lattice of 120x120
or 14,400 sites. Four levels of K were used in the study: 4,8,12, and 24. Thus,
in the latter case, the energy level of a site was dependent on the state of itself
and 24 others (generating around 33.5 million possible combinations per site).
At the start of each simulation run, the all 14,400 sites were given a
random state (either 1 or 0). The sites were then divided into pxp patches
where:
p{1,2,3,4,5,6,7,10,12,15,20,24,30,40,50,60,120}.
The values of p were chosen to exactly cover the 120x120 lattice with no
overlap or missed areas. The simulation model was run 50 times for each value
of p.
Three algorithms were used to search the combinatorial space to find the
best combination of site-states to minimise the global energy level. The
„random‟ algorithm sequentially selected N sites at random from the lattice and
flipped their states (ie 1->0 and 0->1). If the flip decreased the energy of the
patch in which the site was located it was retained. The „fitter‟ algorithm
examined all possible single flips within a patch and then randomly chose one
flip from the set of flips which were found to reduce energy. Patches were
updated sequentially, not simultaneously. Finally, the „greedy‟ algorithm
examined all possible flips in a patch but always chose the flip which created
the lowest energy for the patch.
199
Attempting to minimise total lattice energy over varying levels of K and p
yielded several interesting results. When K=4, the value of p yielding the
lowest energy was p=120 (ie. the whole lattice). However, at higher levels of
K, the value of p was very much lower than 120, ranging from p=5 to p=20. As
complexity (K) increased, selfishly optimising within a small patch led to better
results than attempting global coordination. The researchers also found that the
„greedy‟ algorithm tended to perform better with lower patch sizes than the
other two algorithms.
Rationale for using SELESTE
While ostensibly a physical model of magnets spinning in space, the sites
in the NK model have been reinterpreted to service a wide range of
applications, for example: amino acids in a protein, genes in a genotype, traits
in an organism, components in an artifact, or actors in a game (Kauffman et al.,
1994). The NK model is also beginning to get some serious attention in
economic and organisational research (Kauffman, 1995; Levinthal, 1997;
Rivkin, 1997; Westhoff, Yarbrough, & Yarbrough, 1996). For instance, Rivkin
(1997) interpreted the sites in the NK model as firm resources. Rivkin argued
that the complexity of interactions between resources (determined by the K
parameter) acted as a fundamental barrier to imitation. He was then able to
demonstrate the difficulty of successfully imitating a best practice competitor
using hill climbing and „long jump‟ algorithms on a rugged fitness landscape.
Similarly, Levinthal (1997) used an NK model to represent sites as
organisations. Levinthal demonstrated how small differences in initial
conditions could lead organisations to climb different local peaks, and how
changes in the environment can easily turn peaks into troughs, and vice-versa.
Clearly, the generality and wide applicability of the NK model derives
from the highly abstracted nature of its representational scheme. As a group of
N agents interacting with K other agents to produce specified outputs, literally
any setting where elements interact could conceivably be represented as an NK
model. The wide range of applications to which the NK model has already been
200
applied seems to confirm this intuition. However, how can we be sure that any
particular result is not just an artifact of the NK model? One way to test the
validity of the findings is to replicate the experiment with a different, perhaps
more specific, model.
SELESTE is an abstract organisational model specifically designed to
study strategy issues. While retaining the abstractness of the NK model, it is
more specific to the domain of organisational phenomena. We have noted that
Kauffman (1995) has attempted to generalise the results of the patches
experiment to organisations; claiming that selfish optimisation by divisions in
complex organisations should be more effective than global strategic control.
If, indeed, the „patches‟ hypothesis is valid and applicable to organisations, one
would expect that the effect could also be reproduced in a model of
intermediate realism (ie. a model lying between the abstract NK model and real
organisations). Thus, the primary hypothesis of this study can be stated as:
In SELESTE, local optimisation (patches) will outperform global optimisation at
higher levels of complexity .
Patches of Organisations
Testing the hypothesis required the mapping of concepts in the NK model
onto equivalent concepts in SELESTE. An early examination of the two
models yielded some interesting similarities which we used to create an initial
model that we called SELESTE_A. This model could more correctly be called
a „patches of organisations‟ model rather than a model of organisational
patches (which is considered in the next section). Despite this limitation, the
model still yielded some intriguing parallels with the NK model and thus is
interesting in its own right.
In the NK model, the goal was to minimise energy on a lattice of N sites,
with each site having energy-yielding combinations, or combinations for the
entire lattice. Similarly, in SELESTE, the search space has combinations. This
would suggest an equivalence between N and nFirms and K and(nInds+nSAs).
201
Intuitively, as the number of industries and strategic assets rises, it becomes
harder for a firm to optimise its strategy vis-a-vis other firms. Small shifts in
industry membership or asset ownership can yield large changes in profitability
depending on the strategies of other firms. Thus, the mapping K to
7
(nInds+nSAs) seems to capture the spirit of the K term in the NK model .
It followed from the above formulation that the patch size in SELESTE
should be a function of nFirms. This followed from the fact that the patch size
in the NK model was a function of N. Likewise, as patch performance in the
NK model was measured as the average performance of each site in a patch, an
equivalent concept in SELESTE could be constructed by taking a measurement
of the average profit of each firm in a patch.
Design of PATCHES_A. experiment
We tested the patches hypothesis with PATCHES_A model by
replicating Kauffman‟s original study as closely as possible within the memory
and processing constraints of our computer system. This usually meant scaling
down the size of the parameters from the NK model. For example, the number
of firms was set at 120, whereas the original patches study used a lattice size of
120 x 120. Patch size in the SELESTE replication were drawn from the set,
p={1,2,3,4,5,6,8,10,12,15,20,30, 40, 60,120}. In the NK model, patch size was
the square of each number in the set. Although the size of the search space was
much smaller, the relative scaling of each parameter was the same, so we still
expected to find results qualitatively similar to the NK model.
In the NK model, the value of K was drawn from the set, k={4,8,12,24}.
Similarly, the number of industries and strategic assets in SELESTE_A were
drawn from the set, k=(nInds, nSAs)={(2,2), (4,4), (6,6), (12,12)}. This
formulation preserved the level of complexity used in the NK patches
7
SELESTE is actually more complex because the B and C matrices (which map
strategic industry factors to industries and strategic assets respectively) introduce an additional
layer of complexity. This has been dealt with in the model by setting the probability of a
202
experiment and gave equal weight to the two components used to represent K
in SELESTE. The behaviour of each firm in the system could thus be
represented by a bit string of length, l=(nInds+nSAS), where a 1 signified
membership of an industry or ownership of an asset and 0 signified the absence
of industry membership or asset ownership.
Some minor modifications were also made to the SELESTE model prior
to the experiment. The number of strategic industry factors (NSIFS) was
changed for each run according to the formula: NSIFS=Number of
Firms/Number of Industries. This enabled the munificence of the environment
to be held constant across complexity levels. (Increasing the number of
industries while holding the number of strategic industry factors constant
increased system-wide returns.) In addition, early trials indicated that profit
levels were low at higher levels of complexity. This was remedied by
multiplying the gross profit in each industry by 10 to generate positive profits
for each level of complexity.
Procedure
While the SELESTE landscape was retained, the learning classifier
system algorithm was substituted for a hill-climbing algorithm similar to that
used in the NK model. All firms started the simulation with two bit strings set
to zero - one from the industry membership matrix (A), the other from the asset
ownership matrix (D). Thus, for the ith firm, Ai = Di ={0,0,0,....,0}. Each
round in the simulation run, n bits of each bit string were flipped, so that 0->1
and 1->0. The number of flipped bits, n, was drawn from a binomial
distribution
with
the
probability
of
a
single
flip
equal
to
0.4/(log(currenttime)+1). Thus, there was a higher probability of multiple bit
flips earlier in the simulation, with smaller changes occurring later in the
simulation. Conceptually, firms start by experimenting with large changes and
relationship in the B and C matrices to 0.50 - a figure which maximises the variance in the
matrices. nSIFs=24 for all experiments.
203
then, as the strategy became more refined, concentrated on smaller incremental
changes.
If the n flips increased the average performance of firms in the patch, then a
change was retained, otherwise the bit string reverted to its original state. This
procedure differed slightly from the NK model because only one action per round
could be trialed. The greedy algorithm in the NK patches experiment allowed the
fitness of all one-bit flips from a given position to be tested and the highest
fitness move retained. In the real world, firms lack the luxury to test every
possible move before acting. This is simulated in PATCHES_A by allowing only
one move per round. In the NK patches experiment, it was reported that 50 rounds
were required to reach convergence but this implied testing many actions per
round. To compensate for this effect, each simulation run for PATCHES_A ran
for 1000 rounds. At the end of each run, the sum of profits for the entire 120 firms
was recorded.
Results
The experimental design consisted of 15 patch sizes across 4 levels of
complexity, yielding 60 combinations in a full factorial design. Each
combination was run 50 times to ensure an adequate selection of results at each
level. A graph of these means by patch size and complexity is presented in
Figure 6.9. The graph also indicates the range of variation in profit by including
standard error bars on either side of the mean.
204
125
100
AND
MAJORITY
75
OR
Mean Profit Difference
50
25
0
-25
0
2
4
6
8
10
12
14
16
.
18
20
Complexity (K)
Figure 6.28 Mean Profit by Patch Size and Complexity
The results showed little support for Kauffman‟s findings that smaller
patch sizes improved performance at higher levels of complexity. A casual
observation of Figure 6.9 indicated that, for each level of complexity, profit
varied little according to the patch size. This observation was supported by an
analysis of variance of profit levels for each level of complexity (see Table
6.9). With the exception of the lowest level of complexity (K=4), no significant
difference was found between profit levels at different patch sizes. Although
the analysis was significant for the lowest level of complexity (K=4), a posthoc analysis comparing differences between each pair of means (using Tukey
tests) found a significant difference only between patch sizes 1 and 120. This
result may represent a false positive, or alternatively, may simply be an artifact
of the small model size. Either way, the hypothesis that patch sizes improve
performance at higher levels of complexity could not supported with this data
set.
205
Table 6.14 ANOVA Results
K
4
8
12
24
F(14,735)
1.82*
1.38
0.12
1.12
* - p<0.05
It was also interesting to note that different levels of complexity resulted
in different levels of performance (despite our efforts to standardise profit
opportunities across complexity levels). Profits appeared to fall uniformly as
the level of complexity increased. In fact, the correlation between profit and
complexity was extremely high (r=-0.94, n=3000, p<0.001). Given
standardised profit opportunities, it was likely that this result reflected the
increasing difficulty of search as complexity increased. That is, as the number
of industries and strategic assets increased, it became harder for a firm to find
the right combination of industry membership and asset ownership to maximise
profit. In fitness landscape terms, the landscape became more rugged and the
probability of being trapped on a local peak increased.
Testing Organisational Patches
In hindsight, PATCHES_A was not a satisfactory test of Kauffman‟s
proposition that divisionalisation improved performance. In essence, the
macro-model outlined above only enabled us to ask whether the average
performance of firms cooperating with a small number of firms can earn higher
average profits than firms attempting to optimise performance across all firms
(ie. it is a model of „patches of organisations‟ or organisational cooperation).
The ease of mapping parameters from the NK model to PATCHES_A proved
something of a red herring.
From a strategic viewpoint, it is more interesting whether individual
firms that divide their decision making into patches will outperform those that
attempt to globally optimise their resource allocations. Testing this hypothesis
206
required several significant changes to PATCHES_A, leading to the creation of
a new model, PATCHES_B.
Design of PATCHES_B Experiment
In this experiment, in order to control all effects, the key parameters of
the model were varied with the level of complexity (K). The number of
strategic assets (NSAS), strategic industry factors (NSIFS) and industries
(NINDS) were set equal to K. The number of firms in the simulation
(NFIRMS) was set to 2K.
Half of the firms were labelled as integrated firms. These firms made
strategic decisions according to firm-wide profit considerations. The remaining
firms were organised into patches. In line with Kauffman‟s prediction, each
patch attempted to selfishly optimise its own “patch-profit” without reference
to the rest of the firm. As strategic business units or profit centres in a
multidivisional company are often divided on industry lines (Porter, 1980), it
was decided to set the number of patches equal to the number of industries in a
given simulation run. Thus, each “patch manager” was responsible for
overseeing profits in a given industry.
The experiment used ten levels of complexity, K={2,4,6,...,20}. Three
different decision styles were used to determine decisions (see below). Ten
levels of complexity and three decision styles yielded a total of 30 possible
states to test. Each state was tested 50 times ( for a total of 1500 runs) using a
simulaton that ran for 500 rounds each run.
Procedure
As in the previous model, all firms started the simulation with two bit
strings; one drawn from the industry membership matrix (A), the other from
the asset ownership matrix (D). Both were set to zero, that is, for the ith firm,
Ai = Di ={0,0,0,....,K} with K=NINDs=NSAS. Each round of the simulation
207
run, n parts of the two strings were independently „flipped‟, so that 0->1 and 1>0. The number of flips, n, was determined by sampling from a binomial
distribution with the probability of a single bit being flipped equal to 0.5. Thus,
there was a high probability that many bits in the string would flip. Search
therefore involved large jumps across the fitness landscape in order to move
out of a known poor location to a (hopefully) better profit making area. Search
commenced whenever firms were dissatisfied with their performance, and
stopped when firms became satisfied with their performance.
Integrated and patch-based firms used different criteria for determining
whether a performance was satisfactory or not. For integrated firms, any
strategy (ie. set of flips) that generated a positive net profit for the firm was
labelled „satisfactory‟. However, the presence of multiple patch managers
created a problem for how to determine whether an action was satisfactory or
not for a patch-based firm. The first step for each style was to determine a
„patch profit‟ for each patch. This was taken as the gross profit for each
industry (ie. patch) less a pro-rated asset charge equal to 0.1 times the number
of strategic assets divided by the number of industries. Thus, a patch with a
gross industry profit of 2 units within a firm holding 10 assets across 5
industries would earn a patch profit of: PP=2-(0.1*10)/5=1.8.
The experimental design tested three decision styles. In the first case, a
firm‟s actions were labelled as satisfactory only if all patch managers earned a
profit greater than zero (the AND condition). In the second condition, a strategy
was „satisfactory‟ if the majority of patch managers earned a patch profit
greater than zero (the MAJORITY condition), while in the third condition, a
satisfactory outcome occurred if one or more patch managers earned a patch
profit greater than zero (the OR condition).
At the end of each simulation run, the difference in profit between patchbased firms and integrated firms was calculated (a difference greater than zero
indicating that the patch-based firms were performing better than the integrated
208
firms in the final round of the simulation and vice-versa). Given the results of
the previous experiment, we fully expected that there would be no significant
differences between integrated and patch-based firms over the 1500 runs
whereas Kauffman‟s (1995) theory would predict that patch-based firms should
perform better at higher levels of complexity.
Results
Ultimately, the latter view prevailed. Patch-based firms (using AND and
MAJORITY styles) performed much better than integrated firms. Profit
differences widened with increases in the level of complexity (see Figure 6.10).
125
100
AND
MAJORITY
75
OR
Mean Profit Difference
50
25
0
-25
0
2
4
6
8
10
12
14
16
.
18
20
Complexity (K)
Figure 6.29 Profit Differences by Decision Style and Complexity
Patch-based firms using the third style of decision making (the OR style)
did not perform significantly better than integrated firms (t=-0.06, p>0.05).
Also of interest was the way in which MAJORITY-style firms performed better
than AND-style firms at the higher levels of complexity.
Data on the level of activity in the system was also extracted by
measuring the number of times each firm changed their strategy. A graph of the
209
difference between the activity level of integrated firms and patch-based firms
at each level of complexity and decision style is displayed in Figure 6.11. In
general, patch-based firms underwent about the same number of strategic
changes as integrated firms. However, at the highest level of complexity, the
activity level of AND-style firms increased dramatically. Note that this increase
corresponded with the performance of AND-style firms being overtaken by
MAJORITY-style firms (see Figure 6.10).
500
400
300
AND
200
M ean Count Difference
MAJORITY
OR
100
0
-100
0
2
4
6
8
10
12
14
16
18
20
Complexity (K)
Figure 6.30 Differences in Activity Level by Complexity and Decision
Style
Discussion of patch experiments
The algorithm used in the second experiment can be described as only
minimally adaptive. Firms changed their strategies by simply „flipping‟ a
random number of bits in the industry ownership and asset membership
matrices. When a firm (or patch manager) achieved a positive profit it stopped
adapting (or, in the case of a patch manager stopped requesting changes to the
strategy).
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However, we would maintain that this simple algorithm was a relatively
good test of the effectiveness of patches. Theoretically, the need for a patchbased organisation to satisfy more than one constraint provided an incentive to
continue searching for better strategies long after an integrated firm would have
stopped searching. In particular, the need to satisfy all patch-managers when
using the AND decision style would suggest that a firm needed to find a very
good strategy indeed.
Accordingly, patch-based firms with multiple profit objectives were the
best performers, particularly as the level of complexity rose. Conversely, in the
case of the OR decision style, a positive profit in only one patch was sufficient
to halt search The performance of these firms was not significantly different
from integrated firms. In our relatively munificent environment, an average of
one search was usually sufficient to find a positive profit for integrated and
OR-style firms. In a less munificent environment, one might expect a gap to
open up between the performance of these two styles, particularly in the case
where a positive profit in one industry was not enough to guarantee a positive
firm-wide profit.
We find it interesting that AND-style firms were the best performers at
the lower levels of complexity, only to be overtaken at higher levels of
complexity (K>10) by MAJORITY-style firms. However, there may be a ready
explanation for this phenomena.
As the complexity level grew, the number of patches also grew, and the
size of the problem space increased exponentially. Accordingly, it became
harder to find a satisfactory solution that provided a profit for all patch
managers in the AND-style firm. This was because all industries had to be
active and producing a profit before the AND system halted its search. In a
large system, the probability that all industries would be active in the same
round was very low. In response, the activity level of the system increased
dramatically as the firm was forced to continuously search a vast problem space
looking for a veritable needle in a haystack.
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AND-style firms were thus more likely to be in search mode at the end of
the simulation than MAJORITY-style firms because of the difficulty they had
in finding a satisfactory solution. The random search process ensured that some
of these AND-style firms would be testing strategies that yielded a poor
performance. As firm profits were recorded at a single point at the end of a
simulation run, the average profits of the AND-style group were therefore
depressed relative to the MAJORITY-style group. The latter group, simply by
being able to complete their searches, had more positive performances to
report.
In summary, the results indicated that dividing a firm into patches had the
effect of creating more search activity and higher performance. Requiring profit
targets to be met by all, or the majority, of patches required a firm to engage in
more search than a firm seeking to maximise a single criterion. Paradoxically,
trying to satisfy all objectives in a complex environment was not as effective as
satisfying a majority of objectives. In fact, random search in a complex
environment was just as likely to move a firm away from better profits as
towards them. Kauffman (1995) has reported similar results from his NK
model - ignoring some constraints actually improves average performance.
Limitations & Future Research Directions
At least two areas of the study are highly stylised (or at worst, unrealistic)
and should be viewed as limitations to the external validity of the results. First,
we know that firms tend not to radically or randomly change their strategies
(DiMaggio & Powell, 1983; Rumelt, 1995). Firms are much more likely to be
incremental in their strategy development and to imitate the behaviour of
competitors. Much of this is due to the fact that strategic investments are often
irreversible (Ghemawat, 1991) and that history matters (Dierickx & Cool,
1989).
Second, while there is considerable evidence in the behavioural literature
on decision-making that firms and individuals tend to „satisfice‟ rather than
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optimise in their decision making activities (Cyert & March, 1963; Nelson &
Winter, 1982; Simon, 1957), a key question remains whether integrated and
OR-style firms are being too easily satisfied. If search is so important, perhaps
integrated and OR-style firms are simply stopping their search too quickly. In
reality, companies may never stop trying to improve their position.
Both of these criticisms point to the need for further research utilising a
more realistic algorithm that evolves both incrementally and continuously.
While a learning classifier system has these features, and was our initial choice
of algorithm for this study, modelling patches with a learning classifier system
presented several significant challenges. For example:

Should every patch have a rulebase? If so, how big should the rulebase
be?

If assets are shared between patches, how should conflict between patch
managers over the state of an asset be resolved?

If a patch manager fails to acquire a key asset, how should the rule be
rewarded in the rulebase if the firm is successful (or unsuccessful)?
Given these difficulties, future researchers may wish to bypass learning
classifier systems altogether and opt for something like an aspirational
feedback system (see Chapter 4).
While we were aware of the limitations of our current algorithm, it
should be noted that we deliberately tried to stay close to the spirit of
Kauffman‟s (1994) original hill-climbing algorithm in order to draw
comparisons between SELESTE and the NK model. Kauffman‟s approach
involved testing all possible one-flip moves from a given position and then
choosing the move that yielded the greatest performance increase. Hoewever,
we decided that this was both unrealistic and impractical in the SELESTE
context. It was judged to be unrealistic because the irreversible nature of
strategic commitments typically does not allow firms to implement a range of
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alternatives and then proceed with the most successful option (and in the
process undoing the other options). We felt that simultaneous testing of options
was an even greater sin than the flipping strategy we eventually adopted.
More importantly, Kauffman‟s method was also impractical. In a high
complexity environment, such as K=12, each firm would have to check at least
144 alternatives before choosing the best strategy. Such a large number of
operations would have increased the run time of the simulation from hours to
days, or even possibly weeks. Even so, the best strategy could only be
guaranteed to improve performance if the competitors did nothing Kauffman‟s approach did not consider the potential moves of the other
competitors in the simulation. The flipping method used in both experiments
had the advantage of achieving one action per firm per round while preserving
the trial-and-error spirit of Kauffman‟s original hill-climbing algorithm.
Conclusion
The limitations of the study clearly restrict its external validity and we are
therefore reluctant to draw conclusions about the implications of these findings
for firms in the real world. We are, however, pleased that we have been able to
reproduce two key findings from Kauffman‟s (1995) NK model. This positions
SELESTE as a viable alternative to the NK model for complexity research in
strategy and organisation science.
More work is required to fully test the patches hypothesis. In particular, it
needs to be demonstrated that the effect is not simply an artifact of the search
algorithm and decision rules. Experimenting with a more realistic search
algorithm could test this proposition and improve external validity at the same
time.
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Summary and Conclusion
Post hoc ergo propter hoc1
Contribution to Knowledge
Three objectives were enunciated at the beginning of this study:

to design a strategic landscape

to design of set of human-like adaptive agents to explore this landscape

to use the system to generate new insights into strategy

The study has made contributions to knowledge in each of these
three areas.

Designing a strategic landscape

SELESTE is a model that has been purpose built for strategic
management. Recently, there has been a move towards importing models
from other disciplines, in particular, Kauffman‟s NK model from biology
(Levinthal, 1997; Rivkin, 1997). SELESTE reverses this trend by
offering researchers a model designed around theoretically-grounded
concepts from the strategic management literature. SELESTE also
manages to integrate several of the dominant paradigms in strategy into
one model that offers an intuitively appealing and traditional set of
strategic decisions: What industries should I compete in? How should I
compete in existing businesses (via asset selection).

Moreover, the fitness landscape that arises from the interaction of
elements in the SELESTE model can be made more or less „rugged‟ by
tuning parameters such as the number of industries and industry factors
215
and the degree of turbulence. In terms of complexity, we were able to
demonstrate a level of correpondence between SELESTE and
Kauffman‟s NK model that we believe makes SELESTE an ideal choice
for studies of complexity and complex adaptive systems in the field of
strategic management.

SELESTE has also made several improvements over existing
simulation models in the strategy and organisation literature. The chief
advantage lies in its use of a competitive performance routine. In this
system, a firm‟s profits are dependent not only on its own actions but also
on the actions of its competitors. As competitive interactions are
localised within industries, the potential also exists for economies of
scope to emerge among firms competing in multiple industries. This
compares favourably with studies that rely on exogenous performance
measures (Lant & Mezias, 1990; Levinthal & March, 1981).

SELESTE was also designed as a testbed. This means that
additions and modifications can be made to the core code in order to
explore a wide range of strategic problems. The model was was kept
quite abstract and generic for this purpose. This flexibility distinguishes
SELESTE from other systems that were built for specific applications
and thus lack the ability to be applied to other situations.

Designing artificial agents

At least two contributions to knowledge have been made in the
process of designing artificial adaptive agents to explore SELESTE‟s
strategic landscape. Firstly, support was found for an earlier finding that
learning classifier systems behave in strategic situations much like human
subjects (Bruderer, 1993). In a simple strategic problem, it was found that
learning classifier systems tended to evolve towards the optimal solution
in much the same way as human subjects were observed to do. This was
1
after this therefore because of this
216
primarily due to the fact that learning classifier systems operate according
to operant conditioning principles and thus learn to favour actions that
generate rewards and avoid actions that generate punishments or losses.
Of course, humans are known to be motivated by similar considerations.
Human problems that involve learning via operant conditioning are
probably well-suited to being modelled with learning classifier systems.

In Chapter Four, we also demonstrated that learning classifier
systems were capable of superior performance on a simple strategic
problem when compared with a feedback-based adaptive algorithm that
had previously been used in several simulations in the strategy and
organisation literature. A fully-specified classifier system was shown to
be the best performer but was also impractical for larger scale problems
due to memory constraints. All three algorithms were capable of
outperforming a random hill-climbing algorithm. This suggested that
learning classifier systems were a useful algorithm for exploring the
landscapes we had constructed.

Gaining new insights into strategy

Three representative strategic problems were selected for
investigation using the SELESTE framework. Several theoretical and
methodological contributions to knowledge were made in the three
studies.

In the first study, the relationship between cognitive capacity and
competitive advantage was investigated. An initial experiment failed to
find any relationship between cognitive capacity, environmental
complexity, and competitive advantage. A follow-up study using a
modified algorithm and fixed environment found clear relationships
between cognitive capacity and firm performance. The latter findings
were consistent with theoretical expectations. The study highlighted how
different methods of operationalising the same model can lead to
217
divergent results. The study also provides strong evidence that the degree
of competitive advantage accruing to those with superior cognitive
capacity declines as the absolute cognitive capacity of competitors
increases. This finding had not been anticipated in the earlier literature.

The second study explored the conditions under which imitative
strategies might perform better than innovative strategies. It was based on
a partial replication of a previous study by Lant and Mezias (1990).
Imitative firms were found to outperform innovative firms under almost
all conditions; disconfirming the earlier results of Lant and Mezias. It was
only when the probability of copying an innovation was set at less than
5% that innovative firms were actually able to outperform imitators.
Changes in environmental turbulence and informational ambiguity had
little effect on the primary result. This is a provocative result that
challenges social norms that highly value innovation. Further research
into the power of imitation as a strategy at the population level of
analysis is clearly warranted.

Also of interest in the fact that firms using a random hill-climbing
algorithm performed almost as well as innovative firms using a learning
classifier system. This suggested that the latter algorithm was not
performing as well as it had in earlier trials on simpler problems. Further
research is needed to test the robustness of this finding.

The third and final study used SELESTE to replicate Kauffman‟s
(1994) finding that dividing a complex fitness landscape into selfishlyoptimising patches improved global performance. It was revealed that
firms organised into patches performed better than integrated firms when
they were required to satisfy multiple profit objectives. This was related
to an increase in the search activity of these firms. In addition, at higher
levels of complexity, firms that attempted to satisfy all their constraints
did not perform as well as firms attempting to satisfy a majority of their
constraints. These two results both confirm the observations of Kauffman
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(1995) on the efficacy of patches and build important links between
SELESTE and Kauffman‟s NK model.
Limitations of the Study
We have divided this discussion of limitations of this study into two
parts. The first part concentrates on the limitations of the design of the strategic
landscape used in SELESTE, while the second part concentrates on issues
arising from the use of learning classifier systems to model artificial adaptive
agents. The specific limitations of various studies undertaken can be found in
the discussion sections of the respective studies.
The landscape model
Several design principles were adopted during the planning phase of this
dissertation. The first principle was to keep the landscape model simple,
abstract and generic. The inspiration for the landscape model was the garbage
can model of decision making (Cohen et al., 1972). In the garbage can model,
all entities were modelled as binary constructs embedded in matrix structures;
an approach clearly emulated in SELESTE. We were also keen to incorporate
an integration of the theoretical literature in the strategy field based on the work
of Amit and Schoemaker (1993).
Paradoxically, our approach has been criticised as being both too simple
and too complicated. When some of the early findings were presented to
colleagues in strategic management, a common criticism was that the model
was too simple. Pragmatists wanted labels attached to each industry, firm,
strategic industry factor and strategic asset. “What are the strategic industry
factors in the car industry?”, they asked. “What are the strategic assets of the
computer industry?” There was a feeling that the model was only valid if it was
able to directly model a given firm or industry in order to evolve prescriptions
for strategic action.
219
The justification of our methodology in Chapter 3 specifically rejects the
view that simulations must produce prescriptive outcomes. Our model is
exploratory. It does not seek to inform executive practice in strategic
management, nor does it make any attempt to imply that the patterns of
alignment between industries, strategic industry factors and strategic assets are
in any way modelled on real world cases. Our approach seeks to find lawlike
generalisations that arise when the elements of the model are allowed to
interact in theoretically-defined ways. We hope that these generalisations might
inform the practice of strategic management.
More considered responses took issue with the ways in which the
theoretical constructs were modelled. For example, the decision to model
strategic industry factors and strategic assets as binary constructs was seen as
an oversimplification. It is more likely that firms possess greater and lesser
quantities of a given strategic asset than simply possessing or not possessing an
asset. Similarly, strategic industry factors may vary in strength between
industries. We do not disagree with these observations. They do, however,
greatly complicate the development of the learning classifier system, which
must be reprogrammed to accept real numbers from the environment and then
convert these numbers into internal binary representations before commencing
its normal operation. It was felt that adding this additional complexity to an
already complicated task was not justified in terms of the trade off in
programming time versus potential theoretical insights. A binary model may be
crude but it should send qualitatively similar signals to those of a more
complicated model.
Economists have complained that the model, although competitive, does
not set prices in accordance with the supply and demand for resources. For
example, the acquisition of strategic assets is costless whereas there should be a
market for strategic assets. In addition, the size of excess returns in an industry
is determined exogenously; competition merely determining the division of
excess returns rather than the amount of excess returns. In a market, the amount
220
of excess returns (ie. the size of the pie) would also fall with increased
competition. All this, of course, is true. However, we believed that a fullyspecified economic system was not required shed insights into strategic
behaviour.
There were also many extensions considered for the basic model that
were never implemented (which may be the same thing as admitting limitations
in the original model). For instance, Porter‟s (1980) work suggested that firms
should be able to attempt to change the structure of an industry (ie. by
switching strategic industry factors on and off). However, it was not clear how
this should have been modelled, particularly if there was a conflict between
firms with some wanting a factor switched on and some wanting a factor
switched off.
Many of the possible extensions to the model involved adding greater
realism to strategic assets. Adding elements such as acquisition (or capital
investment) costs, variable usage charges, and exit costs were relatively simple
additions that were overlooked in the interest of simplicity. A second set of
unimplemented features influenced the ease with which a firm could acquire an
asset. As we have seen, the resource-based view considered the inimitability of
an asset a critical part of the theory (see Chapter 2). Assets are harder to acquire
if there a lags in acquisition. Making some assets unavailable to various firms
would also make the process more difficult. We were particularly intrigued by
the notion of establishing path dependencies in the asset acquisition process
(Dierickx & Cool, 1989; Ghemawat, 1991). Under this approach, firms could
not acquire a given asset without already possessing other assets. In turn, the
possession of these antecedent assets could be made contingent on the
possession of other assets and so on ad infinitum. Technological effects could
be modelled by introducing certain assets into the pool at fixed (or irregular)
time intervals. Other asset classes could be retired.
No doubt, the introduction of these innovations are sensible from a
theoretical point of view as they make the model more realistic. The suggested
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modification are also exciting, as they provide a glimpse of the potential of the
simulation approach to study some important theoretical issues in the field
(such as path dependency and the effects of lags). Nevertheless, for the sake of
simplicity they were not included in the model presented in this dissertation. As
such, they are both limitations and future possibilities.
While colleagues in the field of strategic management may view the
model as too simple, the model may also be too complex for others. At a recent
meeting of simulation scientists in the social sciences (Conte et al., 1997),
several speakers commented on the need for a KISS2 approach to simulation.
Models that are too complicated generate results that are difficult to interpret.
Often, there is also a large number of interactions among the entitites in the
model - a fact that makes it difficult to conduct controlled experiments.
In some ways, SELESTE may be too complicated. The introduction of a
construct (ie. strategic industry factors) to mediate the relationship between
industry structure and asset selection, while theoretically desirable, also adds
several layers of complexity to the model. For instance, the existence of this
invisible layer frustrated our attempts to obtain an equivalence between
SELESTE and the NK model as changes in complexity levels also interacted
with the role of strategic industry factors causing changes to the munificence of
the system.
Clearly, finding a balance between a model that is too simple on the one
hand, and too complicated on the other, is a difficult exercise. We believe that
SELESTE represents a workable compromise between the two extremes, but
we also recognise its limitations. We welcome the attempts of others to
improve on this work.
2
Keep It Simple, Stupid!
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The algorithm
The second design principle in this study was to use pre-existing
algorithms (including source code and parameter settings) to model our
artificial agents. It was reasoned that this would avoid the need to justify and
validate coding choices and parameter selection before tackling applied
problems - much as a quantitative researcher doesn‟t need to justify the use of
standard tools such as regression. Accordingly, the learning classifier systems
used in this dissertation were modelled on the algorithm used by Bruderer
(1993), which in turn was based on the work of Goldberg (1983) and Holland
(Holland, 1975; Holland et al., 1986). It is clear that Bruderer (1993) also
shared our interest in using pre-existing code as his own code showed little
variation from that of Goldberg (1983).
However, after more than twenty years of research on biologicallyinspired algorithms such as genetic algorithms and learning classifier systems,
we were surprised to find that there is not a high level of understanding among
computer scientists on the operation of these algorithms. In particular, in the
case of learning classifier systems, it is not known:

Precisely how the algorithm works and the relative contributions of
the various sub-systems (crossover, mutation, reinforcement) to the
overall result;

What types of problems learning classifier systems are most suited
to solving; and

What range of parameters are optimal for a given class of problem.

Work of genetic algorithms has suffered from the same paucity of
basic research. Computer scientists have only recently started to
systematically explore the properties of this algorithm (Forrest &
Mitchell, 1993; Mitchell, Holland, & Forrest, 1994).
223

The paucity of research on learning classifier systems places the
applied researcher in a difficult position when experiments do not
perform as planned (for example, the first experiment of the first study in
Chapter 6). Without reference to theoretical studies, it is virtually
impossible to determine which part of the algorithm failed. It may be that
the problem was not suited to the method or that the parameters were illchosen for that class of problem. In the case of the first experiment, we
believed „premature convergence‟ was the reason that the algorithm did
not work as planned. However, the theoretical literature offers little
insight into whether this condition is common for this type of problem or
indeed whether the problem can be solved at all. The lack of basic
research on learning classifier systems places restrictions on future
research and acts as a limitation of the current findings.

Learning classifier systems are algorithms that learn via operant
conditioning principles. Undoubtedly, humans (and companies run by
humans) also learn to reproduce actions that are rewarded and to
extinguish behaviours that result in negative outcomes, but this is not the
whole story. Humans also learn from observing each other (Bainbridge et
al., 1994). Companies also apparently learn from observing other
companies (DiMaggio & Powell, 1983). In fact, our own results on
imitation confirm that it is a powerful force. Allowing firms to learn from
the actions of other firms would result in a more competitive system and
one in which firms learned faster. This factor should not be overlooked in
future studies.

In addition, companies may not simply act on the basis of feedback
from their own (or others) random actions. A central premise of strategic
planning is that companies can look into the future; seeking opportunities
and avoiding threats in their environment. A key limitation in the study is
that the learning classifier system in SELESTE does not use a lookahead
(or forward planning) mechanism - something that is quite counter-
224
intuitive to our notion of strategy. Riolo (1990) has outlined a scheme for
implementing lookahead planning in a classifier system but he is unaware
of any operational systems that have implemented his ideas (Riolo, 1996,
personal communication). Although we are confident that learning is part
of the way firms explore their strategic landscapes, we recognise that
forward planning also occurs. We look forward, with interest, to reading
about a future study that explores the trade-offs between learning and
planning on strategic landscapes!
Future Research Directions
Some of the future research directions for SELESTE involve addressing
the limitations described in the previous section. Much of the discussion in the
previous section focused on ways of improving the realism of the model and
improving the algorithms used to direct artificial agents. These limitations will
not be repeated but clearly they exist as future research directions in their own
right. Rather, this section will describe some of the new research agendas that
are inspired by the approach adopted in the current study.
Studying rule structure
One of the key advantages that learning classifier systems have over other
artificial intelligence techniques, such as neural networks, is their transparency
of operation. Thus, it is quite possible to examine the differences in the internal
structure of rules that have evolved under different circumstances. This, in turn,
may allow the discovery of lawlike generalisations that have the capacity to
inform real-world practice in strategic management. The closest the current
study came to this approach was to investigate the effect of differences in the
number of rules between two firms. Studying the actual nature of successful
rules that evolve in learning classifier systems clearly has the potential to
further contribute to our knowledge of strategy and should be encouraged.
However, the study of thousands of rules over hundreds of simulation runs
represents an incredibly tedious exercise. Future researchers would be
225
encouraged to construct summary metrics of rule structure before proceeding
too far with such a project.
Computational organisational theory
Another future research project of considerable interest would be to
attempt to link models of firm behaviour (such as SELESTE) with models of
individual or group action within firms (such as the garbage can model).
Researchers in organisation theory have had a long tradition of using
computational models dating back to Cyert and March‟s (1963) seminal work
in the early sixties. These models have addressed issues such as the effects of
decision making styles, organisational structure, and communication systems
on organisational action (Carley & Prietula, 1994; Cohen et al., 1972; March &
Weissinger-Baylon, 1986; Masuch, 1992; Masuch & LaPotin, 1989). The
recent launch of the Journal of Computational and Mathematical Organization
Theory attests to the fertility of this type of research in organisation theory.
The use of computational models from organisational theory may help to
bridge the gap between human action and corporate action that is currently
missing in SELESTE. The actors in SELESTE are firms, but this was not
meant as an anthromorphisation of the firm in any way. We fully realise that
humans form the locus of action in any organisation. Ideally, the actions of
firms in SELESTE should arise from the deliberate decisions of human agents
(as they do in the real world) rather than by chance.
For instance, the garbage can model of decision-making represents
decision processes as the interaction of problems, solutions, actors and choice
situations mediated by structural considerations (Cohen et al., 1972; Masuch &
LaPotin, 1989). Perhaps the rulebase in SELESTE could be conceived as a set
of possible solutions to strategic problems, possessed by various actors, each
having a preference for their own solutions. The selection of a strategic action
from the rulebase could be resolved in choice situations according to garbage
can principles. It is interesting to speculate whether the decisions of firms in
226
SELESTE would appear more realistic or “human”. Such studies hold the
promise of improving strategic decision-making in firms.
Models combining behavioural and strategic issues could also address
some important theoretical issues. In particular, the investigation of
organisational structures and forms that facilitate quality strategic decision
making would be of interest, as would investigations concerning factors that
impede or hinder effective strategic action. Rumelt (1995) has compiled a long
list of factors that create inertia in organisations and prevent them from
undertaking strategic change. Research linking strategic and organisational
processes may further extend our knowledge in this area.
Population ecology
In Chapter One, we noted that biologists have been attracted to the use of
simulation models to study ecological processes. The label „artificial life‟
(Levy, 1992) has been applied to the products of this endeavour. It is not
surprising that biologists have turned to simulation, given the timescale of
evolutionary change and the relative lack of complete datasets in the physical
world.
Organisational ecologists have also complained about a lack of
appropriate data in the area of strategy and organisational studies (Baum &
Singh, 1994). Institutional records detailing the births and deaths of
organisations, with associated data on explanatory variables, are rare. Indeed, it
would seem that whole industries are off-limits because their pattern of
mergers and acquisitions makes the determination of births and deaths
problematic.
Like their counterparts in biology, organisational ecologists may benefit
from studying simulated populations. In SELESTE, the births and deaths of
organisations can be carefully defined and unambiguously measured.
Moreover, contaminating factors, such as mergers and acquisitions, can be
prevented from occurring. Naturally, explanatory variables can be studied
227
experimentally and measured accurately. The features of simulation that
attracted biological ecologists should also appeal to organisational ecologists
and warrant future research attention.
More basic research
The studies in Chapter 6 were carefully selected to demonstrate the
versatility of SELESTE in addressing a diverse range of research topics. The
studies were also rather applied in nature in line with a preference in strategic
management for addressing practical problems. Unfortunately, as we have
already observed in the limitations section, this push for applied results in
strategy and computer science often results in slow progress on important
theoretical issues.
An important future research direction for SELESTE is the systematic
investigation of the model‟s properties. For example:




Do small models behave the same as large models?
Under what conditions do learning classifier systems prematurely
converge?
What role does turbulence play?
Does the initial starting position of firms affect results?
Theoretical research forms the foundation of applied research. It enables
researchers to more easily explain anomalous results and to design experiments
that consider the fundamental limitations of the model. While clearly an
unglamorous side of research, it is nevertheless important because it enables
research to accumulate and grow rather than reinventing the wheel at every
stage.
More applied research
This dissertation has strongly defended the right of SELESTE to stand
alone as an exploratory simulation model without real world calibration or
validation. This is not the same thing as saying that SELESTE can never be
calibrated or validated with real world data. A number of colleagues have
expressed an interest in modelling real industries, real strategic assets and real
228
firms with the hope that SELESTE might somehow yield up prescriptive (and
profitable) strategic actions for their clients.
Clearly this approach represents a possible future research direction.
There are, however, several major problems that must be overcome before the
benefits of such a project can be realised. Chief among these problems is the
identification and quantification of strategic assets held by a firm. It is one
thing to say that strategic assets exist (as we have done in SELESTE), it is
another thing to identify, catalogue and value the existing (and future) strategic
assets of a group of firms in an industry. Over time, it is also difficult to
determine the cost of a strategic asset, its rate of depreciation or the cost to
acquire a new strategic asset. Similar problems also exist with the identification
and treatment of strategic industry factors. Therefore, bon chance to all who
venture down this path.
Conclusion
In a recent paper on the future of organisation science, Bill McKelvey of
UCLA, suggested that analytical or computational models should command
25% of the published output of organisation science, in line with similar figures
in the physical and biological sciences (McKelvey, 1997). Strategic
management, of course, is nowhere near this figure. The use of computational
models is still in its infancy.
The development of SELESTE represents a first step towards filling this
void. One major contribution of this dissertation has been the construction of a
theoretically-grounded model that uses artificial adaptive agents to study
strategic landscapes. It is hoped that SELESTE, or variations of SELESTE, will
form the basis of a cumulative research agenda in the field.
229
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