Towards an Understanding of Elusive
Organizational
Phenomena:
Organizations as Complex Adaptive
Systems
Par: Jad Bitar, Research associate Chaire
management stratégique international
Walter-J.-Somers, HEC Montréal
Cahier de recherche N° 06-27-02
January 2006
ISSN: 1711-6309
______________________________________________________________________________
Copyright © 2006. La Chaire de management stratégique international Walter-J.–Somers, HEC Montréal. Tous droits
réservés pour tous pays. Toute traduction et toute reproduction sous quelque forme que ce soit est interdite. Les textes
publiés dans la série Les Cahiers de la Chaire de management stratégique international W-J.- Somers n’engagent que
la responsabilité de leurs auteurs. Distribué par la Chaire management stratégique international Walter-J.-Somers,
HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 2A7.
Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Table of Content
Abstract ............................................................................................................................... 1
Résumé................................................................................................................................ 1
Introduction......................................................................................................................... 2
Organizations: Purpose and Tool........................................................................................ 3
Closed Systems ........................................................................................................... 4
Open Systems.............................................................................................................. 4
Systems, Complexity and Chaos................................................................................. 5
Organizations as Complex Adaptive Systems .................................................................... 7
Wholeness vs. Interdependence ...................................................................................... 9
Natural Systems .......................................................................................................... 9
Organizational Systems............................................................................................. 10
Regulation vs. Self-Organization.................................................................................. 11
Natural Systems ........................................................................................................ 11
Organizational Systems............................................................................................. 12
Hierarchies vs. Heterarchies ......................................................................................... 14
Natural Systems ........................................................................................................ 14
Organizational Systems............................................................................................. 15
Adaptation vs. Co-evolution ......................................................................................... 17
Natural Systems ........................................................................................................ 17
Organizational Systems............................................................................................. 18
Design vs. Emergence................................................................................................... 20
Natural Systems ........................................................................................................ 20
Organizational Systems............................................................................................. 21
Conclusion and Some Implications .................................................................................. 23
Bibliography ..................................................................................................................... 25
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Le véritable voyage de découverte ne consiste pas à chercher de nouveaux paysages, mais
à avoir de nouveaux yeux.
Marcel Proust
Abstract1
In this paper, we explore how organizational theory and strategic management can benefit
from looking at organizations using a complex adaptive systems metaphor. For this we
look at ants’ colonies and individual ants behavior to identify key characteristics of
complex adaptive systems (interdependence, self-organization, heterarchy, co-evolution
and emergence) and contrast them with organizational systems. For each characteristic,
we examine its possible implications for organizational design. In particular we focus on
how these characteristics can help us understand emergent organizational phenomena
such as organizational capabilities. Finally, we discuss these findings and identify
implications for managers and researchers.
Résumé
Nous identifions des caractéristiques essentielles des systèmes adaptatifs complexes
naturels (auto-organisation, heterarchies, interdépendance, co-évolution et émergence) et
les contrastons avec des systèmes organisationnels. Pour chaque caractéristique, nous
discutons sa pertinence pour les systèmes organisationnels et les implications possibles.
En particulier nous explorons comment ces caractéristiques peuvent nous aider à
comprendre certains phénomènes organisationnels complexes tels que les capacités
organisationnelles et la performance. En conclusion, nous discutons ces résultats et
identifions des implications pour les gestionnaires et les chercheurs.
1
I am indebted to the many reviewers of this document. It is thanks to their ideas, suggestions and
encouragement that this paper is in its present form.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Introduction
Organizational studies have progressed considerably in the decades since Cyert and
March’s (1963) seminal work. Organization scholars have since considerably developed
the field by contributing new models and theories of organizations (Thompson, 1967;
Galbraith, 1974; Morgan, 1986) and established links between organizational forms and
performance (Mintzberg, 1979). However, from a practitioner’ perspective, the role of
managers in today’s organizations seems to be, day after day, a more complex and
daunting task (Huy, 2001; Westley, 1990) to deliver an even harder to reach
organizational performance. Several external factors that might be at the root of this have
been examined in the literature (Bettis and Hitt, 1995; Emery and Trist, 1965) such as
globalization and turbulence. On the other hand, internal elements have been the object of
research with a prescriptive perspective, i.e. what are the elements that need to be
modified to “respond” to those changes? The literature suggests several programs such as
business process re-engineering, leadership development, and knowledge management
(Hauschild et al., 2001; Hammer and Champy, 1994). Existing organizational models
such as Thompson (1967) or Mintzberg’s ideal types (1979) have been powerful in
guiding researchers’ exploration of fundamental internal elements (such as structure,
power or communication), but they seem to be of limited help when dealing with elusive
phenomena such as competitive advantage or capabilities that are posited today as the
source of organizational performance (Wiggins and Ruefli, 2002; Spannos and Lioukas,
2001; Porter, 1985). In a turbulent and hypercompetitive (D’aveni, 1994) setting,
competitive advantage is rarely sustainable, hence capabilities need to be frequently
renewed and organizations continually adapted.
Mintzberg’s models are rich description of organizational “states” that are rarely attained,
and if attained, these are usually transient. These models do not help us understand how
organizations “tend” towards such states and how they change to reach them. In their
review of organizational change, Van De Ven and Poole (1995) remind us that
identifying organizational change mechanisms is a central quest for researchers in this
discipline, and that it is fraught with difficulties. For this, qualitatively different models
of organizations are needed to help researchers and practitioners understand the
“networks of causality that link managerial, organizational, and strategic outcomes of
success” (Miller, 1999). For this, we are encouraged to converse between disciplines,
since organizations are multi-dimensional/multi-level phenomena, hence in need of
multidisciplinary approaches (Scott, 1987). The emergence of the “New Science”, offers
an opportunity for building such dialogues between the social and the physical sciences
that can provide us with new perspectives of organizations. Recent applications of the
“New Science” to organization research (McKelvey, 1998) suggest that there may be
valuable lessons to draw for organizational theory in dealing with elusive phenomena
such as organizational capabilities and performance. While there is no such thing as a
clearly defined theory, the “New Science” is a trans-disciplinary field that has attracted
researchers from several disciplines such as biology, chemistry and physics.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
We want to explore possible implications of the “New Science” and especially the
concept of complex adaptive systems on organizations and organizational change in the
context of complex phenomenon. The purpose of this paper is to identify new principles
for organizational design that could help managers deal with elusive phenomena such as
organizational capabilities and performance. Guided by complexity theory, we will
identify essential characteristics of complex adaptive systems (CAS) and examine their
relevance to organizational theory. Specifically we will start with a review of
organizations, different models of systems as applied to organizations. Then we will
explore the degree to which the essential characteristic of CAS can be imported from
natural systems to organizational systems. Later, we will discuss these findings and try to
identify some implications for managers in such settings. Finally, we will conclude and
suggest some recommendations for future research.
Organizations: Purpose and Tool
What is an organization? Some authors suggest that organizations are collectives of
individuals working towards a common goal hence patterns of coordination, cooperation
and communication (Barnard, 1938). Others suggest that organizations are the tools
enabling groups of individuals “to effectively coordinate their efforts and get things
done” (Nohria, 1991). Several explanations of the existence of organizations have been
offered in the literature such as the agency theory of the firm (Eisenhardt, 1989; Coase,
1937), the behavioral theory of the firm (Cyert and March, 1963), or the Knowledge
Based View (Nonaka and Toyama, 2002; Conner and Prahalad, 1996). Organizations,
according to the neo-classical theory of economics, are complements to markets.
Organizations are created to respond to markets’ failure to deal with particular resources
such as loyalty or trust. From an organizational theory perspective, “uncertainty appears
as the fundamental problem for complex organizations, and coping with uncertainty, as
the essence of the administrative process” (Thompson, 1967). Hence, from such a
perspective, organizations are artifacts for uncertainty reduction since a minimum level of
reliability is necessary to deliver predictable outputs.
Organizations have been frequently described as systems (Crozier and Friedberg, 1977;
Thompson, 1967). A System is defined as an interdependent and connected group of
elements that form a whole. The nature of this unity emerges from the interaction of these
components and is not embodied by any single component. Two major schools dominate
the systems perspective. The most popular – the closed-system perspective- views
organizations as having limited communications with their environments. This view is
characterized by a rational model which advocates control and predictability through
hierarchy, a simplified reality, a controlled environment and no individual agency. The
other, the open-system perspective, has gained in popularity recently with the rise of
concepts such as “Hypercompetition” (D’Aveni, 1994) and “turbulence” (Emery and
Trist, 1965). This view adopts a natural-system model, bounded-rationality and continual
interaction with the environment.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Closed Systems
Closed-systems interact in a limited way with their environment. They are characterized
by a rational model of decision making (Allison, 1974; Simon, 1997), where the system’s
objective is to reduce uncertainty and filter out noise to the upper echelons of the
organization where “rational” decisions can be made. Such a view values and advocates
control and predictability through hierarchy, routines, and other standard operating
procedures to handle a simplified reality which a controlled environment and the
abstraction of individual agency lead to. Cause-effect interactions are assumed to be
linear, and tight controls are seen as the best way to eliminate uncertainty. The goals of
the system guide its design, its activities and its resource allocation.
Organizations as closed-systems have been at the source of classic managerial concepts
such as “scientific management” (Taylor, 1967) and Weber’s “bureaucracy”. It depicts
organizations as self-contained and self-regulated (cybernetics) where objectives can be
attained by managers focusing on internal activities and tasks linked “mechanically”.
Organizations are viewed as collections of information sharing mechanisms to reduce
uncertainty and facilitate decision making. The main objective is to minimize fluctuations
to enhance efficiency. Individual agency is neglected. Organizations are hence technical
systems that have a maximization role through rational decision making - “the feelings
and idiosyncratic characteristics of individuals [are] ignored” (Hafsi, 2001). In this
perspective, the whole (the system) is the dominant element. What the system needs is
imposed down the hierarchy on elements who satisfy those needs.
The implication of this view for researchers is that it encourages a Cartesian paradigm for
analysis or disjunction, on top of which modern science is built. This analytical practice labeled objective - consists of reducing empirical phenomena and their complexities into
neatly decomposable elements, describing the isolated parts and interpreting it following
the Cartesian model’s precepts: evidence, analysis, order and enumeration. The analysis
phase strongly encourages reductionism2 while the order phase supports a mechanisticdeterministic3 view that often leads to the mutilation of complex phenomena (Morin,
1998; Drazin and Van de Ven, 1985).
Open Systems
This perspective, contrary to the closed-systems’, has the individual as its unitary
element. Organizations are constructions emanating from the interactions of individuals
who voluntarily accept to cooperate (Barnard, 1937). The open-system perspective views
organizations as sets of interdependent parts which together make up a whole because
each individual element contributes some inputs to the system and receives some inputs
back from it, and is itself interacting with a larger system. Systems are hence in continual
2
“diviser chacune des difficultés que j'examinerais, en autant de parcelles qu'il se pourrait, et qu'il serait
requis pour les mieux résoudre.”
3
“de conduire par ordre mes pensées, en commençant par les objets les plus simples et les plus aisés à
connaître, pour monter peu à peu comme par degrés jusques à la connaissance des plus composés.”
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
interactions with their environment where the system is embedded and within which it
evolves. This environment is composed of multiple elements such as other organizations
and individuals, each of which is involved in networks of interactions with other
elements. A system’s boundaries are permeable allowing information to flow from and to
the ecosystem. Some question separating the organization from its environment. They
argue that environment-system is a continuum and both are in fact one (Morgan, 1986).
The perspective’s focus is both on individuals and organization. The organization is a
collection of individuals and a hierarchy of levels of different elements such as groups
and departments. With individuals taken into account, uncertainty is brought back in the
picture and individual agency as a source of intrinsic uncertainty becomes essential for
understanding elements such as informal structures or cultures. Hence, this focus on
individuals transforms technical systems into socio-technical systems (Emery and Trist,
1965). This uncertainty is not seen as stochastic or as errors committed by bounded
individuals, but rather as tentative adaptive responses at the local levels by creative
individuals in non-routine situations, e.g. mutual adjustments (Mintzberg, 1979; Crozier
and Friedberg, 1977; Crozier, 1960). These local fluctuations lead to the emergence of
“shadow” organizational elements. This interplay between the formal and informal
“systems” is the main engine of deviance (Alter, 2001; Mintzberg, 1979) or rather
innovation. The co-alignment of a system’s elements, not only individuals but also
“institutionalized action”, in time and space become essential for an open system to
“survive”, i.e. to continue to exist as a whole (Thompson, 1967; Barnard, 1938).
The closed-systems view assumes that organization can be decomposed into elements
that can be observed independently, and the knowledge derived from these single
observations can be summed up (aggregated) to make sense of the whole. In contrast,
open-systems researchers have to address the complexities of the phenomenon at hand
and to deal with simultaneous variables’ modifications at the same time. This requires a
different approach than the traditional Cartesian one to understand organizations.
Systems, Complexity and Chaos
Organizations are built by individuals’ actions and build the context for individuals’
actions. They are purpose and tools, “both an articulated purpose and an established
mechanism for achieving it” (Miles, Snow, Meyer and Coleman, 1978). As Weick (1995:
31) describes it “people are very much a part of their own environments. They act, and in
doing so create the materials that become the constraints and opportunities they face.”
Hence, we will adopt a view of organizations as made of two fundamental and
complimentary elements, organization and organizing. The first, “being” or the
“organization” part is the dominant one in the literature and among practitioners. The
other, “becoming”4 or the “organizing” part of organizations, hold, we believe, much
promises for both researchers and practitioners, especially when dealing with dynamic
phenomena and change. Weick (1998) summarized it best: “To understand organization
is to understand organizing or, as Whitehead (1929) put it, to understand ‘being’ as
constituted by its ‘becoming’”. Barnard (1938) was one of the earliest organizational
4
Von Bertalanffy (1968) suggested this concept, “becoming”, at the individual level.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
authors to advocate such a view of organizations as “being” and “becoming”, recognizing
the importance of both a static view and a dynamic view. The “being” (organization)
perspective describes stable states (Mintzberg, 1979; Thompson, 1967) whereas the
“organizing” perspective describes the continual becoming of the organization: being
changed by its elements and its ecosystem, and changing its elements and ecosystem at
the same time. Adopting Barnard’s perspective, Penrose (1997) defines firms as
knowledge repositories to be leveraged in a “productive opportunity set” and institutions
that develop and manage knowledge. For her, a firm is an organization and “organizing”,
a state and a process, being and becoming, and those two sides are inseparable. The
“organization” and the “organizing” facets are intertwined, interact and expand the firm’s
“productive opportunity set”.
Adopting a pure static view can hence be detrimental to organizational researchers since
it is a mutilating perspective. Complementing it with a “becoming” perspective will allow
us to elucidate phenomena that still prove elusive for researchers and practitioners. The
implications of such a perspective are several but share a common characteristic: dealing
with paradoxes. The first such implication is the need to conceive of organizations as
nested systems with multiple levels where a lower level is “changing” more rapidly and
affecting the focal level, which itself is also constrained by the level above changing at a
slower pace. Hence the first paradox, an organization can be changing and constant at the
same time; turbulence at one level can be accompanied by order or stability at another. A
second implication is the necessity to bring back individuals as the fundamental unit:
organizations are the result of the interaction of individuals. Changes at the individual
and interaction level can cascade up the levels to affect change in the organization as a
whole (Barley, 1990). While organizations are constrained by supra-organizational forces
as advocated by institutional theory or evolutionary economics (DiMaggio and Powell,
1983; Hoffman, 1999), they are also transformed by individuals’ interactions (Oliver,
1991). Hence, another paradox, managing the external “fit” between organizations and
their environment (Andrews, 1971; Ansoff, 1965; Porter, 1980) needs to be accompanied
by managing an internal “fit”, between the different organizational elements
(Chakravarthy, 1982; Mintzberg, 1979). Finally, interactions between individuals within
and across levels lead to patterns of cooperation and competition. These paradoxical
patterns are essential for the linking of internal and external dynamics where local and
global adaptations, while potentially conflicting, need to be optimized simultaneously
(Selznick, 1957).
Hence, these two models (open and closed systems) complement each other and are
necessary for a better “view” of organizational reality (Thompson, 1967). While
organizations viewed as states fixed in time can help their understanding, so is their
viewing as dynamic entities in continuous motion. To use a metaphor, organizational
snapshots as well as continuous organizational videos, can help researchers better grasp
the complexity of organizational phenomena. This view is supported by systems thinkers
(Senge, 1990; Bateson, 1980; Ackoff, 1970), who advocate the necessity for
organizational researchers to adopt a mind shift that highlights the interrelationships
between elements and not only the linear cause-effect. This will augment a researcher’s
toolbox with a dynamic view rather than uniquely a static view. The purpose is not to
replace one view by the other; rather it encourages researchers to use both views since a
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
dynamic view can be complementary to a static view. As de Rosnay (1975) explains,
“systems thinking” is not the ability to see from afar distant realities, nor is it the ability
to see small things better. It is rather the ability to see things more completely, closer to
their “reality” to better appreciate them.
In the last decade, several authors have tried to push systems thinking forward, adding to
such established concepts as cybernetics, homeostasis and feedback loops, new concepts
derived from the “New Science”: chaos theory, catastrophe theory and complexity theory.
While there is no such thing as a clearly defined theory, the “New Science” is an
interdisciplinary field that have attracted researchers from several disciplines to work
together such as at the Santa Fe Institute (Waldrop, 1992) where biologists, physicists,
economists and other scientists have converged to collaborate. According to MitletonKelly (1998) “the theories of complexity provide a conceptual framework, a way of
thinking and a way of seeing the world. They also provide a different explanation of how
the world is, which is different from the Newtonian and Cartesian paradigms.” These new
concepts have been mainly borrowed from the natural sciences such as chemistry
(dissipative structures, bifurcation points), biology (emergence) and physics (edge of
chaos); and they could offer new perspectives to understand organizational evolution and
change (Anderson, Meyer, Eisenhardt, Carley and Pettigrew, 1999, Anderson 1999,
Pascale, 1999; Kauffman, 1995; Thietart and Forgues, 1995; Levy, 1994).
The popularity of the “New Science” has led researchers to explore how to apply its
lessons to the social sciences. One of the early examples is Arthur’s (1996) application of
feedback loops to economics. Organization theory also has been, in the last decade,
enriched with several concepts derived from the New Science. While some researchers
recommended the use of mathematical modeling (Cheng and Van de Ven, 1996) to
reproduce the behavior of organizations in-silico, others have actually tried to identify
organizational elements such as “strange attractors” (Stacey, 1996) and still others
suggested using these new concepts in a metaphorical way (Lissack, 1997). One of the
most interesting concepts to be developed in the wake of the new science is the concept
of “complex adaptive systems” (CAS).
Organizations as Complex Adaptive Systems
A CAS is a special type of system where simple interactions (positive and negative
feedback loops) between elements of the system can lead to complex behavior, the
emergence of order from disorder without any centrally designed intelligence. Complex
adaptive systems are autopoietic systems (self-organizing) that are made of autonomous
and intelligent agents capable of learning and adaptation (Anderson, 1999; Pascale, 1999;
Beinhocker, 1997; Holland, 1995; Kauffman, 1995; Levy, 1994). The agents’ behavior is
driven by global and local objectives and adaptive rules that can be specific to agents; and
agents are influenced by other agents’ behaviors. While agents can have different local
objectives and rules, their interactions lead the system as a whole to self-organize in
somewhat stable and recurrent patterns. The CAS “learns,” “maintains” and “transforms”
itself through interactions, entry, exit and transformation of individual agents.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Complex adaptive systems can be found everywhere in nature and society from ants’
colony to the market economy. An ants’ colony is a good example of a natural complex
adaptive system. “The basic mystery about ant colonies is that there is no management. A
functioning organization with no one in charge is so unlike the way humans operate as to
be virtually inconceivable. There is no central control. No insect issues commands to
another or instructs it to do things in a certain way.” (Gordon, 1999, vii). If there is no
single “conductor” in a colony telling ants what to do, when to do it, how and with
whom, how they get to do anything? Local interactions between individual ants with very
limited knowledge and information about the world and the colony provide them with
opportunities to “make decisions, and somehow it all coalesces into colony behavior”
(Gordon, 1999). Hence, without any blueprints or plans, a group of ants establishes a
colony by adopting such a dynamic:
1. Each individual ant goes about its own “work”, interacts and interchanges
information with other ants through pheromones (indicating food, danger, etc.).
2. These local interactions regulate the ants’ behavior and lead to self-organization
3. without any predefined hierarchy but rather a heterarchy
4. where an ant’s behavior continually co-evolve with others’ (execute tasks according
to needs as identified by local interactions)
5. which lead the colony to co-evolve with its surroundings (e.g. enhanced security if
danger is sensed) and hence the emergence of colony wide patterns.
We believe that viewing organizations as Complex Adaptive Systems can help enhance
our understanding of organizational evolution and change. Adopting such a perspective
highlights the importance of individuals, organizations and also the interactions between
individuals and organizations. These interactions lead to the emergence of other
organizational phenomena such as power and culture which are essential for the
understanding of organizations. An organization as CAS is hence a set of interdependent
elements that makes it up and which is itself an interdependent element of a larger
system: the environment. The focus is not only on organizations and individuals but
include also interrelations between organizational elements.
In the following sections, we highlight five key properties of complex adaptive systems
and discuss their origins in the New Science and suggest how they are relevant to
organizational research. Some of these properties have been the subject of discussions in
the literature though others have drawn less attention. We emphasize these particular
properties because they are, we believe, critical to understanding complex organizational
phenomena and most interestingly because their application is rather generalizable to
different complex adaptive systems and can be applied at different levels of analysis such
as the organizational level or institutional field.
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Wholeness vs. Interdependence
Natural Systems
A complex adaptive system as an entity is the product of the interactions among its
elements where the whole is greater than the aggregation of the elements. These
interdependencies (couplings) are essential elements for understanding the dynamics of
the system as a whole and not only as a collection of separate elements. As we saw in the
ant’s colony example, there is no such thing as an ants’ colony if it was not for the
behavior of individual ants, interacting locally to solve specific problem such as foraging
for food or picking up dead ants to dump in “cemeteries’. “Each ant scratches and prods
its way through the tiny world of its immediate surroundings. Ants meet each other,
separate, go about their business. Somehow these small events create a pattern that drives
the coordinated behavior of colonies” (Gordon, 1999, viii).
A system’s elements are not just the agents that interact with one another, but also
include the relationships among these agents and the environment. A change in any
element can drive a change in other elements, which can cascade up or down to other
levels and end up impacting the whole. Hence, internal elements within CAS’ can affect
their surroundings as well as they can be affected by them. This exchange allows them to
adapt and evolve. In this perspective, an ant’s entities are not only the ants going about
their business and interacting with each other but also pheromones traces, the colony’s
interaction rates and patterns of behavior such as foraging in groups. A change in the
interaction rate (e.g. below a certain threshold) will indicate the disappearance of ants and
signals a danger. Hence, this will trigger a change in the ants’ behavior and lead to seek
refuge in the nest, leaving potential foraging areas to other colonies or species. A simple
change in a colony’s element such as density can propagate across individuals and lead to
a change of local behavior, impacting system’s behavior as well as the environment.
In some perspectives, such as autopoiesis (Morgan, 1986), this fact is driven to its logical
conclusion and where it is asserted that the environment is not only outside the system
but is inside it too. Ashby’s law of requisite variety states that external variety has to be
matched with a similar degree of internal variety. Applying Ashby’s Law to a CAS, not
only indicates the need for a system’s variety to “fit” the external variety, but alludes to
an interaction between internal and external environments, which are coupled to various
extents5. Hence, the tighter the internal-external couplings, the less clear is the boundary
separating them. For an ants’ colony, foraging grounds are weakly coupled to the nest
location: the location does not shift with increased or diminished foraging results. The
border, in this case, is clearly delineated between a colony and its environment (foraging
grounds). However, in the case where a nest is flooded, the behavior of each ant is
immediately changed and is tightly coupled to the flooding. Here, literally and
figuratively, the border between the nest and the environment (flooding) is hard to
discern. For a CAS, the environment is not a single, homogeneous entity that is clearly
5
When systems have no margin of maneuver i.e. the environmental constraints are immediately felt, they
are described as tightly coupled with the environment whereas they will be described as loosely-coupled if
their margin of maneuver is large.
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
defined but rather a collection of niches, entities and other phenomena continually
shifting and interacting with a CAS in tight or loose couplings (Kauffman, 1995), hence
having different effects on different elements of a CAS. “Neighboring colonies search the
same places for food, so pressure from neighbors can starve out a tiny new colony or
keep an older one from reproducing” (Gordon, 1999).
Organizational Systems
An organization as a complex adaptive system is not only a collection of individuals
working alone but also interacting - collaborating and competing- at different levels.
These organizational elements through their interactions organize the system. They not
only interact with each other, but they depend on each other e.g. through the distribution
of functions and tasks for individuals. Organizations as CAS highlight the importance of
the elements and the system as a whole as well as the interactions. The interactions
between the elements in a loosely coupled (Cyert and March, 1963) and fluid way
become themselves an essential source of variation and hence an important element to
better understand organizational change. The interdependence of organizational elements
is not only driven by cooperation or competition but by both dynamics at the same time
on different levels and between different elements (Moore, 1996).
An organization’s elements are not only the individuals interacting with each others, but
also the patterns of interactions and outcomes of theses interactions such as structure –
formal and informal- (Mintzberg, 1979), culture (Nohria, Joyce and Roberson, 2003),
processes and systems (Bartlett and Ghoshal, 1995a, 1995b). A change in any of these
elements can trigger changes in other elements up and down the organization in other
levels and end up having an impact on individuals, organizational capabilities and the
organization as a whole. The numerous examples in the literature and in the press
regarding the profound impacts of restructuring, changes in leadership and process and
systems redesign (Eisenhardt and Galunic, 1996; Barley, 1990). These elements, their
interactions and their outcomes are at the source of organizational capabilities and
organizational performance (Miller, Fuchs, Mifflin and Whitney, 2000; Pascale, 1984).
Hence, while individuals are the basic elements (agents) that constitute organizations, it is
them and their interactions that are essential for organizational performance.
Individuals, being an essential element of organizational systems, there is at the most
basic level, an existential need for organizational systems to interact with their
environment to acquire these “building blocks”. An organizational system’s environment
is not a homogeneous entity but rather a supra-organizational system of elements
interacting with each other (Weick, 1995). Because of this, the nature of the
organizational system boundary will be fuzzy and in some cases will be arbitrarily
defined. The fuzziness of the boundary is not a cause of the interchange but rather a
symptom of the organizational system’s organizing nature. Fluctuations in the
environment, hence, could be sensed and trigger some actions internal or external to the
organizational system. This interchange with the environment is another source of change
within the organizational system. Change and innovation (positive or negative) are hence
essential characteristics of an organizational system (Macintosh and MacLean, 1999).
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
Defining an organization’s environment as a collection of disparate elements leads us to
view it as a relative concept not an absolute one. It is constantly created by organizations
actions and acts to constrain them: “Organizations are both creators and prisoners of their
environments” (Burgelman, 1991). The environment hence is not exactly the same to all
organizational systems, even ones that are “grouped” together in single sectors or
industries by researchers. Since the environment, as defined above, does not include the
focal organization, it is necessary different for each focal organization. Having
highlighted the importance of the interactions between elements in a system, the relativity
of environments leads us to suggest that the impacts of the environment on a CAS are not
the same on all organizations within a single “group” (sector, industry, etc.). The larger
an organization has an impact on its environment, the more differentiated is its
environment from the rest of the organizations within its group. The smaller an
organization has an impact, the more similar is its environment within its group.
Regulation vs. Self-Organization
Natural Systems
Closed systems researchers advocate shielding the system from environmental
fluctuations by closing it up since disturbances in the environment can negatively impact
it. The system is regulated purposefully and change does not happen for change sake, it is
triggered by a rational entity in pursuit of some objectives (Scott, 1987). Complex
adaptive systems are somewhat different: they can self organize. A CAS’ boundary, as
discussed above, is not hermetic but permeable to the environment, exposing its elements
to outside interactions, allowing them to change at one level or another. The interactions
between internal and external elements as well as between internal elements increase the
“fitness” of the elements while driving the system to change, to self-organize (Kauffman,
1995), enhancing the sustainability of the whole.
This self-organization is not “chosen” or is an objective of the system but it is the result
of the changes in the system’s elements. This property of CAS is observed in several
physical and biological entities such as Bénard cells or termites nests (Waldrop, 1992).
Order (i.e. organization) does not occur as a result of some central design, pre-defined
blueprints or requests from a hierarchy, but instead it is self-generated by the system as
its internal elements reconfigure themselves locally. The system’s elements adjust to
perceived “disturbances” and adapt to the changes in the environment using feedback
loops which cascade changes within a level or across levels, leaving a CAS stable at
some levels while changing at others.
An ant’s colony is such a self-organizing system where there is no pre-existing blueprint
of plan for how the nest is going to be built or how the colony is going to be managed.
“The queen of an ant colony has a misleading name. […] she has no special authority or
privilege. There are no kings” (Gordon, 1999: 13). Rather forager ants “walk out of the
nest looking as if they know where they are going, and usually head straight off the
mound and out into the surrounding vegetation. [...] Often the first few hundred foragers
will fan out, but later on the base of the fan, at the nest side, becomes more linear, and
eventually the foraging “trail” is shaped like a long blob with a narrow trail” (Gordon,
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1999: 34). No one has pre-identified the trail and ordered the foragers to build it or use it
rather as Machado muses about “no hay camino, se hace camino al andar” i.e. there is no
trail, the trail is made by foraging. The trail is self-organized from the interactions
between hundreds of foraging ants going each about its local business.
A CAS can continuously change its elements while the whole remains stable or constant.
This “order out of chaos” property, leading to stability, can be confused with equilibrium
(Mittleton-Kelly, 1998). There is, however, an important difference between static and
dynamic (balance) equilibrium6. Static equilibrium is the consequence of the interaction
of forces whose resultant is null while balance is simply the effect of counterbalancing
forces which allow a certain dynamics to emerge. A system in static equilibrium cannot
change its current state without the intervention of an external force while a system in
balance can maintain “equilibrium” albeit a dynamic one while changing internally. An
ants’ colony remains largely in balance while at the same time the average lifespan of an
ant is around a year (Gordon, 1999), i.e. each year on average the colony changes (part of
its population) while it remains the same.
This balance is neither automatic, nor constant. It requires the continual “consumption”
of energy – a continual exchange of information with the environment- for the system to
maintain its balance. The system becomes “structurally coupled” (Cyert and March,
1963) to its environment. Pressures to constrain and regulate the system can lead to
changes within the system, depending on its structure and composition. While negative
feedback loops can dampen these effects and in some cases even eliminate them, their
combination with positive feedback loops allows it to self-organize and develop new
ways of sharing and handling information, as well as acting upon it, i.e. in fact changing
its structure and composition (Arthur, 1994). Hence, while external pressures can be a
regulating, homogenizing force, a CAS self-organization property can be an engine of
innovation, chronically churning new capabilities. An ant’s colony has in normal
conditions specific capabilities such as gathering environmental scanning (patrolling) and
food gathering (foraging) that allows it to survive and develop. However, in special
conditions, an ants’ colony can self-organize and deploy capabilities that “fit” the new
conditions. In an ants’ colony “[…] foraging and midden work tend to be done by
different individuals. But this was true only under stable conditions. When conditions
change, ants switch tasks […]. If extra food appears, an ant that was doing mitten work
will switch tasks to become a forager” (Gordon, 1999: 98-99).
Organizational Systems
Organizations have been described as socially constructed entities, collections of
individuals assembled under a distinctive banner pursuing common goals (Scott, 1987;
Selznick, 1957; Barnard, 1938). Hence organizational systems are essentially
teleological, coordinating the efforts of several individuals to achieve specific goals such
6
Kauffman (1995) gives the example of a chemical reaction attaining equilibrium, a statistical property
since the number of A and B molecules are constant but molecules are flipping from one state to the other
thousands of time per minute.
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as financial, market or production objectives. In their review of the literature, Van De
Ven and Poole (1995) identify four “building blocks” or motors that explain
organizational change: life cycle, dialectic, evolution and teleology. These motors
implicitly entail a minimum level of rationality: to plan the cycle, to synthesize from a
thesis and antithesis, to select the right elements that maximize a function and to identify
the goals to go after, respectively. This suggests that organizational change is necessary
conscious and purposeful, it is regulated somewhat rationally to adjust the organizational
system’s activities towards its mission. An organizational system’s activities are
motivated by its objectives and its elements are constantly adjusted to better pursue them.
Organizational systems are also described as responsive adaptive systems (Stacey et al.,
2000; Selznick, 1957) molded and shaped by internal and external demands.
Organizations often modify an element either because it has accomplished its objectives
(e.g. strategy or plans) or because it can endanger the organization’s performance (e.g.
core rigidities, Leonard-Barton, 1992). These modifications are often necessary to
maintain the organizational system’s performance and therefore its preservation. Once
established, organizations change their unifying purposes. They tend to perpetuate
themselves and in the struggle to survive, they may change the reasons for their own
existence (Barnard, 1938). These changes are described in the literature as rational, preplanned and carefully executed but are often results of local interactions and agents
adapting locally to ground conditions (Pascale, 1984). Hence, while organizational
systems are often portrayed as functional tools, they also have a “life of their own”,
changing their elements without any “conscious” organizational decision. Illustrating this,
Pascale (1984) provides an incisive description of how Honda bikes conquered the
American market. Academics adopting traditional organizational models explained
Honda’s success through a rational perspective where every element was carefully
regulated: careful analysis to select opportunities, planning the moves to capture those
opportunities and disciplined execution to implement the plans. Pascale shows that such a
perspective was not accurate and explained Honda’s success thanks to Honda’s
employees’ local interactions (with customers who loved the “Supercubs”), subsequent
adaptation and self-organization to develop the necessary capabilities (convincing Tokyo,
developing new sales channels, developing new marketing campaign, etc.) to conquer the
American market.
This property of organizational systems echoes self-organization in natural systems. A
new organizational “state” is attained without being the result of any central design or a
decision-making process in a hierarchy, but instead is self-generated by the different
organizational elements which reconfigure themselves locally to increase their fitness. In
the case of Intel, sales managers adapting locally to their customers needs transformed
themselves7 influenced production managers through feedback loops built in the
organizational system, cascading up and down the organizational system to change
themselves, their activities and their output, leading with time to transforming the whole
organization.
7
They focused on chips instead of memory DRAM which led to changes in different elements at the sales
level such as new knowledge is required, new processes and activities, new sales channels, etc.
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Hierarchies vs. Heterarchies
Natural Systems
Complex adaptive systems are not hermetic uniform entities separated and isolated from
other elements; rather they are composed of hierarchies and heterarchies of subsystems
and elements, a structure which can lead rapidly to a high level of complexity, not only at
the system level, but at an inter-system level. Hierarchies involve the organizations of
elements in distinct levels where the function of a level is the basis for operation of the
level above (e.g. the function of a cell defines how the tissue it forms operate -liver cells
vs. heart cells). Heterarchy (Hedlund, 1986) refers to the organizational mode of a
system’s components that is qualitatively different from a hierarchy. In a heterarchy,
elements can be connected to any other elements without specific rules such as unity of
command, single-point of communication flows upwards or pre-identified centers of
decision making (Mintzberg, 1979). In fact, in a heterarchy8 there is no above or below,
or a top or center, but rather there are different centers that are qualitatively different.
Communication can flow in any direction and any element can be connected to any other.
Changes in elements in one system not only affect elements in the same system, but could
affect other systems.
A hierarchy is characterized by authority based on position, clear communications
channels and specialized roles. Hierarchies allow a system to grow with minimum
complexity by adding new levels of different groups which reduces the amount and types
of information exchanged. However, these characteristics force it to be slower because
while authority is centralized, capabilities cannot be since they are inherently distributed
across a mosaic of individuals’ skills (Cohen and Levinthal, 1990). A heterarchy
conversely is characterized by open communication channels, authority based on
expertise and local initiatives to adapt to ground uncertainties. While this could suggest a
utopia where every agent communicates with every other, complexity theory suggests
that a high connectivity rate between organizational elements, beyond a tipping point
(Gladwell, 2002), can edge a system towards chaos (Holland, 1995; Kaufmann, 1995).
Any small perturbation in a highly interconnected system can indefinitely reverberate
across the system. Imagine an ants’ colony where every ant communicates with every
other. The response rate for any event will be so rapid and overwhelming that the ants
will continually be in a “crisis” mode and will never get down to “business”, leading to
the colony’s rapid demise.
However, complex adaptive systems develop a patch-like (Kaufmann, 1995) internal
network structure where patches of tightly connected elements are loosely-connected to
other patches (Mintzberg, 1979). Hence due to their hierarchy/heterarchy and
wholeness/interdependence properties, CAS can develop patch-like network structures
which allow them to change, adapt and self-organize without tipping into chaos regularly.
A CAS patching property is valuable for “two quite different reasons. Not only do such
systems quickly achieve good compromise solutions under conflicting constraints, but
8
This model is similar to Mintzberg’s Adhocracy.
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they should also track the moving peaks on a changing fitness landscape very well if the
existing environment changes. If external conditions alter, a rigidly ordered system will
tend to cling stubbornly to its peaks. Poised systems, by contrast, should cope better with
shifts in the landscape” (Kauffman, 1995).
An ants’ colony, “it must collect and distribute food, build a nest, and care of the eggs,
larvae and pupae” (Gordon, 1999: 117). In a stable environment a colony has different
ant patches, role-based (e.g. queen and foragers), geographical and functional (e.g. clean
the nest after a flooding). There is a somewhat clear division of labor within a quasihierarchy. However, ant colonies are in a continuously changing world and must often
adapt to extraordinary conditions. The heterarchical communication system, where each
ant can communicate with any other in its patch and adapt to local conditions by
changing its role (task), allows the colony to adapt to new conditions in its environment
(e.g. other neighboring colonies) as well as internal changes9.
Organizational Systems
An organization is a coordinated system of individuals’ activities in pursuit of common
goals (Barnard, 1938). Although individuals are the building blocks of an organizational
system their cognitions, personalities, and local realities differ. Additionally
specialization within organizations, such as the functional distribution of tasks and goals,
for efficiency reasons leads to the formation of different groups and subsystems. Hence,
organizational elements in a CAS are not connected to all others but rather are tightly
connected to a group and each group is loosely connected to other groups. Connections
between elements at one level of a CAS drive the dynamics at other levels (Rivkin and
Siggelkow, 2003). Additionally, specialization shapes the organizing system in different
ways by influencing elements such as structure and culture. In turn, these elements
define10 the level of stability of the system. Hierarchies (e.g. the formal structure in
bureaucracies) and heterarchies (e.g. the informal structure in bureaucracies) within an
organizational system initiate internal competitive and cooperative dynamics. Hence,
organizational systems as CAS are not homogeneous entities but rather communities of
different constituencies (or patches), each with its own local interests and challenges that
contribute to the overall organizational system objectives (Bartlett and Ghoshal, 1997).
Organizational systems viewed as CAS are composed of hierarchies and heterarchies in
unison. These hierarchies and heterarchies define communication networks and help
reduce the organizational system’s complexity since they serve as informational filters,
eliminate unambiguous accountability and clearly identify expertise “sites” within it
where different types of knowledge are conserved (Romme, 1996). Hierarchies support
the “organization” part and help increase the predictability (but reduce adaptability) of an
organizational system by clearly identifying
1. authority and roles to deal with business as usual, as well as
9
“[…] ants in young colonies behave just like ants in old ones, [but] the colonies seem to become more
staid and prudent as they get older and larger” (Gordon, 1999: x).
10
Up to a point since these are elements in dynamic equilibrium. They are stable but are continually
changing, negotiated and fought over.
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2.
“escalation” procedures (through its hierarchy) to handle unexpected events.
Heterarchies, on the other hand, increase adaptability but lower predictability since
information can flow in unpredictable manners and can lead to non-organizationally
planned activities, i.e. “organizing” at a local scale or innovating. An organizational
system’s hierarchies and heterarchies constitute its internal network and is an important
element to understand the propagation of information, such as the process of
institutionalization (Oliver, 1991). An organizational system’s hierarchies might be
clearly delineated, while its heterarchies are continually negotiated at the local level
where authority is continually in flux and sensemaking dynamic.
A CAS hierarchies and heterarchies are not separate elements that define the system as a
whole (“being”) but are also interacting elements that shape the interdependence among
its elements, leading it to change (“becoming”). What we mean is that a hierarchy or a
heterarchy within an organizational system is not a fixed element but is fluid depending
on the context. For example, coordination can be highly centralized (hierarchy) e.g. the
typical resource allocation process (Bower, 1974) or a highly decentralized (heterarchy),
e.g. the IDEO model (Kelley, 2001). The higher the task uncertainty, the additional
coordination needed since the amount of information to be processed is greater to
maintain a performance level. Hence, uncertainty could cause increased or decreased
hierarchy in organizational systems. Depending on its context, an organizational system’s
hierarchies or heterarchies are emphasized.
Paralleling the ants’ colony capability to self-organize in response to changes, Lissak
(1997) describes the case of BioTech, a biotechnology company employing a technique
called “flocking” which is the “ability of the organization to recognize good opportunities
and to flock resources around those opportunities […] Having the ability to flock, is
having the ability to take advantage of opportunity” (p. 210). Clear and stable hierarchies
do not preclude such organizations to adapt to changing conditions, owing to their
heterarchies. “[…] researchers […] are free to seek out their own resources among the
company’s components; and second, BioTech has an institutionalized group (which they
label operations) whose function is to satisfy the bureaucratic demands of the
organization and keep the bureaucrats away from these select researchers” (p. 211).
In such a setting, borders between hierarchies and heterarchies are nebulous. BioTech’s
hierarchies (operations) provide its stable patches with the needed support. Yet,
researchers working on a stable patch can drift to start a new one if an opportunity
presents itself. BioTech’s heterarchies, caused by this freedom of researchers to capture
opportunities as they see fit, constantly support the formations of new patches. BioTech
researchers’ ability to identify opportunities and rapidly adapt locally helps the whole
organization to continually transform itself to adapt to new environments by constantly
developing new organizational capabilities. Such a heterarchical model without a
counterbalancing hierarchy can have a negative influence on performance. The more
opportunities present themselves, the more fragmented (increasing number of new
patches) the organization will be. Chasing too many opportunities too frequently without
having the resources to deliver on enough could cause its failure.
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Adaptation vs. Co-evolution
Natural Systems
Adaptation is often seen as a process through which organisms change in response to new
conditions to enhance their chances of survival. This suggests an image of organisms
rationally identifying changes in their environment and then tinkering with themselves or
the environment to become fitter. Such an image, while widely held, is erroneous since
adaptation is a population-level concept and not an individual-level one. Adaptation has
its origins in Darwin’s natural selection concept where variations in some individuals are
propagated in a population. Retrospectively “selected-out” individuals are labeled as
having adapted to a new environment. Adaptation therefore is a result, rather than a
process, accounting for nothing more than variations in individuals that become
predominant in a population (Holland, 1998). The concept as such has no elementary
biological significance as much as performance does not have any elementary
organizational significance e.g., temperature does not have any elementary significance in
physics. It is merely a description of average speed of the measured object’s molecules
(The Economist, 2005). Hence, adaptation is an evaluation of past actions and is valuable
in “so far as it accounts for the ‘preservation of favored races’. It throws no light upon the
origin of the variations with which races are favored” (Torrey, 1998).
Applied to an ants’ colony, adaptation looks something like the following. A colony uses
more food to produce fertile ants since a queen weighs ten times more than a regular one.
In hostile environments (e.g. intense competition from neighboring colonies) some
colonies may not grow large enough to reproduce as much as other colonies since ones
with more workers have more food. Hence, colonies with large number of ants will
contribute more colonies to the next generation and their queen’s specific gene pool will
get “selected out” by the environment. Thus, these would have been labeled as having
adapted while other colonies with small population would be extinct. This process does
not allow for ex-ante prediction of which gene pool will spread.
Hence adaptation in the common sense does contribute to our understanding of survival
since it is the survival of individual systems that interest us and not populations (or
ecosystems). For this, it is important to focus on stem level processes that can account for
individual variations leading to better fitness. CAS’ structure (hierarchy/heterarchy), selforganization property and coupling (tight/loose) to their environments endow them with a
critical capability: a flexibility to change in a somewhat synchronized fashion with their
environments. This capability allows CAS to survive internal and outside variations and
hence carry on “being” through changes in its elements (e.g. entry and exit of individual
agents), changes to its elements (e.g. changes to agents), changes in couplings between its
elements as well as changes in coupling between the organizational system and its
environment. This capacity to change is paradoxical in the sense that a system needs the
stability of the “being” as a state at time t0 as well as the potential instability of the
“becoming” as a process to be able to evolve to a new “being” state at time t1. This
paradox is deeper when we take into consideration that while a CAS is stable at time t0, it
is only at some levels, since other levels are continuously changing their constitutive
elements, i.e. the CAS’ elements are replaced while the whole remains the same. For
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
example, an individual’s human body continually changes its cells on average every few
days but individuals remain exactly who they are, at least in the short term. For this
change to occur some levels must remain somewhat stable such as blood pressure, sugar
levels or body temperature.
CAS ability to change in synch with their environment is a fundamental capability and is
labeled: co-evolution. Co-evolution is a process where changes in an element depend (to
varying degrees) to changes in other linked elements and will cause change in yet other
ones. It is the result of agents (e.g. ants in a colony) trying to adapt, fuelled by a
propensity to increase a fitness function. In CAS, elements “keep changing in a neverending race simply to sustain their current level of fitness” (Kaufmann, 1995) always
recreating a new stable state (order), itself leading the system to further change. Coevolution can be found at all scales within a system, and is described as endogenous
when it affects patches within the system or exogenous when it is the whole system
interacting with its environment (Mittlelton-Kelly, 2003). Co-evolution is a natural,
autonomous and “unconscious” process, affecting a system and its environment.
Organizational Systems
While adaptation in natural systems is an erroneous application of a population-level
concept to an individual-level one, using this concept in an organizational context is
appropriate since an organizational system has the capability to modify itself and its
behavior. Adaptation has been described in the literature as an organizational process that
enhances organizational fitness to changing environments, and thus increases long term
survival chances (Miles et al, 1978). It is rationally and consciously11 planned, centrally
implemented, and requires organizations to manage their purpose and to minimize the
misfit between purpose and capabilities (Chakravarthy, 1982). However, the literature is
replete with cases of organizations unable to adapt. Miller and Friesen (1980) suggest that
this is due to momentum which is a central feature of adaptation. Momentum originates
in the synergy or coherence of an organizational configuration produced by tightlycoupled organizational elements such as purpose and capabilities (Mitleton-Kelly, 2003;
Chandler, 1992; Thompson, 1967). When such a configuration is in line with the
environment, increased organizational performance reinforces this configuration, thus
giving it a momentum to persist. For such a gestalt to change, several organizational
elements need to be modified at the same time and in a significant manner. This could
explain the punctuated-equilibrium model of organizational change observed by Gersick
(1991) and Tushman and Anderson (1986). Thus adaptation in organizational systems is
rational and also reactive since organizational change lags environmental change.
Hafsi and Demers (1997) describe the case of Hydro-Québec once one of the most
admired companies in the province. From its inception in 1963, Hydro-Québec
accumulated success after success on many levels: technical, economical, social, cultural
11
Miles et al (1978) even suggest that top managers need to solve three “problems” of organizational
adaptation: entrepreneurial (purpose), engineering (operational systems) and administrative (managing
uncertainty).
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and even political. Hydro-Québec seems to have been able to brilliantly adapt to its
environment and might have twisted it to its advantage. This successful adaptation
allowed the emergence of a gestalt symbolized by Hydro-Québec employees’ perception
of its identity: a mighty engineering organization managing large-scale projects. This
gestalt was present everywhere in the organization: its identity, its purpose (electricity
production), its leadership (engineers), its capabilities (build huge dams in tough
environments) and its activities (engineering and scientific research). While adaptation
was a positive process for Hydro-Québec, it also led to a gestalt that engendered a
momentum for the perpetuation of a single dynamic. Hence, Hydro-Québec did not start
adapting to the energy crisis that followed the oil shocks of 1974 until…1981 when a new
president was brought on board.
Hence adaptation, a rational and conscious process of change, can drive organizations to
perpetuate an invariable change, leading to an “architecture of simplicity” (Miller, 1993).
In turbulent environments, where rationality is limited, the process of adaptation can be
inappropriate to organizations (Winter and Zollo, 20002; Lewin, Carroll and Long, 1999).
However, the continual interaction of organizational elements allows organizational
systems to perpetuate themselves through, what Thompson (1967) calls, co-alignment in
space and time, or co-evolution. The co-evolution principle applied to organizational
system can shed some light on organizational systems’ sustainability even in turbulent
environments. Co-evolution guides different elements of a system to adjust to others in a
never-ending dance of competition and cooperation (Hafsi, 2001). This subtle iterative
interplay within and between organizational systems allow organizational systems to selforganize and evolve with their environments. Organizations are affected by their
environments but also affect and shape them (Macintosh and Maclean, 1999). Hence, in
organizations as CAS, adaptation is complemented by co-evolution, caused by the
coupling of CAS’ elements with other elements within and outside it. It is to some
degrees an autonomous and “unconscious” process that drives an organizational system
to seek, but never attain, “coherence” within its elements and with its environment
(Mitleton-Kelly, 2003).
This quest for coherence influences the pattern of change in organizational systems.
While some elements and interactions seem to be stable at times, they are, nonetheless,
changing in minimal terms and could with time become unstable leading to uncertainty
and unpredictable outcomes. Organizational systems, while having their elements coevolving, do not change in “real-time” with other elements in their ecosystem but rather
at different thresholds or tipping points (Gladwell, 2002) that trigger change. Hence while
a system’s elements are in continual evolution, the system as a whole appears to be
stable, fixed at a state S0, but at certain points in time (bifurcation points) changes from
one state to another. This adds further support to the punctuated equilibrium model of
organizational change that reconfigures the system (Gersick, 1991; Beinhocker, 1997)
since shakeout periods might lead to the disappearance of some organizational elements
and the appearance of new ones. However, the nuance here is that a punctuated-model is
observable at one level while continuous change is taking place at other levels in the
organizational system. Thus, in organizations as CAS, continuous change in lower levels
of an organizational system can lead to a punctuated change at a higher level which
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reconfigures the system and triggers again continuous changes at lower levels, feeding
the co-evolutionary dynamics of the system. Therefore, the evolution of organizational
systems while possibly predictable in the short term might become highly unpredictable
in the long term depending on a multitude of internal and external factors.
Design vs. Emergence
Natural Systems
In his voluminous book, Wolfram (2002) wonders about the ability of natural systems to
produce so much novelty and complexity compared to human artifacts. Wolfram suggests
that the difference stems from the fact that human artifacts are usually designed for a
single purpose with a simple behavior (i.e. “fool proof”). Human artifacts are teleological
by design. Design, according to Wikipedia, is both a noun and a verb depicting the result
and process, respectively, of “developing a plan for an aesthetic and functional object”.
Hence, design is rational, premeditated and functional, which suggests the presence of a
designer. In contrast, natural systems, where there is no such designer (at least from a
complexity perspective), cultivate novelty (e.g. structures, behavior) through emergence.
Emergence is “the arising of new, unexpected structures, patterns, properties or processes
in a self-organizing-system” (Zimmerman, Lindberg and Plsek, 1998). Emergent
phenomena are unexpected results, at a higher level of a system, of elements interacting
at lower levels. They are qualitatively different (different rules, patterns and behavior)
from their lower-level components and have a life of their own. Emergence happens at
the system level not at the individual level. A good example is the slime mold which
spends most of its life as distinct single-cell units, each with its own independent life.
However, when the conditions are ripe, these thousands of single-cell units coalesce in a
single, larger organism with a new form, behavior and properties.
Emergence, a prevalent phenomenon in natural systems, is a direct result of CAS selforganization and co-evolution properties. Simple rules of interactions and self-reinforcing
feedback between a system’s elements lead to sustainable patterns, increased complexity
and the emergence of a new complex behavior at a higher-level. Emergence cannot be
predicted or controlled: “[...] the behavior of the overall system cannot be obtained by
summing the behaviors of its constituent parts” (Holland, 1998: 122). In an ants’ colony,
there is nothing in the behavior of individual ants that can help us predict the emergence
of a colony, its structure or its behavior. Individual ants, following simple rules (forage,
do mitten work, patrol), interact constantly at a local level and continuously adapt to new
local conditions (e.g. food fields low stop foraging, do mitten work) which lead to the
emergence of the colony. Getting a group of ants together is not sufficient for a colony
emerge, it is essential for these to interact following simple and coherent rules.
Hence a colony is not only a collection of individual ants; it is much more than the sum
of each ant. The whole is greater than the sum of the parts, and it exhibits patterns (e.g.
roles) and structures (e.g. nest, foraging paths) that are not pre-designed or accomplished
by any single individual, they rather emerge spontaneously from the interaction of the
parts. Emergence highlights the importance of multi-level interactions in CAS. Lowerlevel interactions can bubble up and lead to higher-levels transformations which affect
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back the lower-levels. It can be imagined, as suggested by Varela, the emergence is the
transition from simple, local rules of interactions between elements to system level
phenomenon or states that affect the whole system.
Organizational Systems
Several models of organizational change have been proposed in the literature from
Lewin’s classical unfreeze-change-freeze to more elaborate recent ones such as Hammer
and Champy’s reengineering (1994). Organizational design has been one of the main
managers’ tools to change and adapt an organization to better fulfill its objectives. Design
is the ability to transform the organization by tweaking the appropriate organizational
elements such as structure, coordination mechanisms or communication channels. “In the
case of organizational structure, design means turning those knobs that influence the
division of labor and the coordinating mechanisms, thereby affecting how the
organization functions- how materials, authority, information, and decision processes
flow through it” (Mintzberg, 1979: p.65). Organizational design hence assumes managers
discretion and reflect the command and control paradigm where rational individuals’
desire for certainty leads to the belief that they can make things happen. We suggest that
such an approach can be applied to the mechanistic part of organizational systems, the
“organization”. However, applying such a paradigm to the “organizing” part may trigger,
what Perrow calls, normal accidents since in complex systems applying such a
perspective is a guaranteed “logic of failure” (Dorner, 1996).
In organizations as CAS, elements continually interact and co-evolve at different scales.
They do so through the process of self-organization at the local level which gets
replicated across the hierarchies and heterarchies to the system level leading the CAS to
co-evolve with its environment through emergence. Emergence is the transition from
local principles of interaction between elements of the system (e.g. fulfill customers’
orders) to organizational-level characteristics (e.g. we are in these markets) encompassing
the whole system (Mitleton-Kelly, 2003). Emergence is a widely observed phenomenon
in social systems such as cities, economy, markets and organizations (Johnson, 2001). It
is the result of multiple interactions and feedback loops within an organizational system
where new properties or structures arise with no “premeditation” or pre-existing
blueprints. Selznick (1957) was well aware of such phenomena in organizational systems
and he suggested that “taking account of both internal and external social forces,
institutional studies emphasize the adaptive change and evolution of organizational forms
and practices. […] New patterns emerging and old ones declining, not as a result of
conscious design but as natural and largely unplanned adaptations to new situations” (p.
12). In organizational settings, emergence is a much more prevalent phenomenon that in
natural systems since “the possibilities for emergence are compounded when the
elements of the system include some capacity, however elementary, for adaptation or
learning” (Holland, 1998: 5).
Rindova and Kotta (2001) provide a detailed account of how Yahoo! continuously
morphed into new forms not because its executives were designing it for the future rather
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
they were obsoleting12 its past. Moreover, Burgelman (1991) describes the transformation
of Intel from a memory DRAM manufacturer into a chip manufacturer without any
“conscious” organizational decision to do so. From a high level analysis, Intel or Yahoo!
might have looked like they were stable organizations having found a static equilibrium
to settle in. However, as the analysis of Burgelman (1991) and Rindova and Kotha (2001)
show, the equilibrium was in fact dynamic, different elements were changing but in
balance giving the impression of order and stability at one level but were fomenting
change and upheaval at higher levels. Interactions enhanced (or limited) by the
organizational system’s hierarchy and heterarchy enhanced some stimuli and hence
reinforced some organizational element (e.g. sales of chips at Intel) and limited some
others (sales of DRAM), even halting them, through positive and negative feedback
loops. The organizational system’s self-organization property allowed different patches
(e.g. production and sales) to self-organize to adapt to changes in the environment (clients
demand). While Intel seemed to be stable as a whole, profound changes were taking
places at different levels which were propagating across levels, until the emergence of a
new “whole”. The emergence of a new Intel was official, when its executives became
conscious of these changes and officially adopted them. Intel became a chip manufacturer
and exited the DRAM business.
12
“We are in the business of obsoleting Yahoo!” Jerry Yang, CEO and founder of Yahoo, (Rindova and
Kotha, 2001).
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Conclusion and Some Implications
It is obvious today that there is a critical need for new tools that can help researchers and
practitioners make sense of the dynamism in organizations. Organizations as CAS can
offer researchers a powerful analytical and synthesis tools to capture the dynamics and
interactions of emergent organizational phenomena such as organizational capabilities.
From a managerial perspective, adopting organizations as CAS, we can only suggest
some directions for further exploration but no definite practices. Our analysis suggest that
organizational systems change while dependent, in the short term, on their history and
equifinal dynamics, defy prediction in the longer term. Multiple interactions have a direct
impact on their capacity of change. Such a view emphasizes a continuous model of
change and a punctuated model of change at different levels of analysis, due to selforganization and co-evolution. Additionally, it stresses the important role of internal
factors to influence organizations thanks to co-evolution and interactions much more that
managers might recognize. However, at the same time it highlights the limits of what
they can accomplish if the hierarchy and heterarchy dampen their actions. Finally, this
capacity to change is paradoxical since organizations need the stability of the “being” as a
state coupled to the potential instability of the “becoming” as a process to change.
The implications for managers may be numerous but we will focus on some that we find
key. The first implication, and one that complements a notion that has been repeatedly
suggested in the literature, not only there is no “one best way” to design organizations
(Nohria, 1991), but managers should explore the use of design for emergence. What we
are suggesting is a reformulation of Selznick’s suggestion that “the tendency to
emphasize methods rather than goals is an important source of disorientation in all
organizations. It has the value of stimulating full development of these methods, but it
risks loss of adaptability and sometimes results in a radical substitution of means for
ends” (Selznick, 1957: 12). Instead of using organizational design to attain a functional
objective, managers should try to design organizations able to design for functional
objectives. This is similar to the difference between teaching as providing students with
information and knowledge, and teaching as providing them with the tools, the
motivation and the objectives to learn. Biotek as discussed above seems to be doing just
that. To be able to implement such a design, the second implication for managers is to be
aware of what is going on in the “organization” and the “organizing”, to identify positive
deviants (Dorsey, 2000) or local innovators whose actions might be amplified. This leads
to a third implication for managers: expect surprise even when you are in control, since
interactions at different levels might produce new elements at any time. Hence, and this is
the fourth implication, managers need to be in a constant management of paradoxes
where individual and collective goals need to be harmonized, competition and
cooperation encouraged, and exploration and exploitation funded all at the same time.
The last implication for managers is the importance of time. Time has two aspects: the
time between events (duration) and timing (when events happen). Time in its two aspects
is an essential concept to understand dynamics in CAS. The duration aspect is necessary
to make sense of how new elements emerge from the interactions of different elements,
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Towards an understanding of elusive organizational phenomena: Organizations as Complex Adaptive Systems
e.g. the time for interactions, retroactions and other feedback loops to go about their
cycle. This aspect highlights the importance for managers to be able to function on
different timescales since we have a naturally tendency to measure things according to
our human timescale. Some organizational phenomena will be ultra-rapid compared to
our human timescale. These we usually label as crisis and we amplify them by managing
them on the wrong timescale. Some others will be extremely slow compared to our
human timescale that we will perceive them as not happening such as changes in
organizational cultures. These, we have a tendency to disrupt with our lack of patience.
On the other hand, timing’s importance is highlighted by the interaction between
“organization” and “organizing”. An action (e.g. restructuring) in an organizational
system can have positive effects at one time while devastating effects at others. An
action, depending on the state of the “organization” at time t0 where this action is
undertaken can be amplified (positively or negatively) by the “organizing” or dampened.
Hence, since organizational systems are not static entities, the timing of an action will be
an essential determinant of its effects.
Moreover, adopting such a perspective can help researchers shed some new light on how
organizations sustain themselves and change, and the role of individuals and their
interactions within organizations lead to emergent phenomena that defy direct
observations and have proven elusive. This will require a major shift on how we view
these entities: organizations and “organizing” interacting continuously. Understanding
organizations as CAS requires identifying patterns within the “mess” of reality (Waldrop,
1992), patterns of structuring, behavior and outcomes. These patterns could help
researchers understand the present functioning of organizations. In a CAS, there is no
dependent or independent variable, every element can interact with any other (Mintzberg,
1979). Not only designed elements even emergent ones. From an academic perspective,
organizations as CAS will encourage researchers to examine official information sources
and unofficial ones such as organizational myths and legends, and other persistent
memories since the organization and the “organizing” concepts with their corresponding
artifacts are continually interacting. Another important issue is the problem of language
since we are trying to describe a continuous phenomenon in encapsulated, discrete terms.
Inherently such a process will scar and limit our descriptions (Weick, 1995). Stories,
figures, models and other artifacts and tools should become standard part of our arsenal.
Finally, it is essential for researchers when treading in such unchartered territories to
avoid “the mapping of principles from the natural sciences onto social systems. Mapping
would […] assume similarities between those systems studied by the natural and social
sciences, which may not exist, and which could lead to an ontological category mistake.”
(Mittleton-Kelly, 1998).
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