INTERNATIONAL JOURNAL OF ORGANIZATION THEORY AND BEHAVIOR, 16 (3), 368-392
FALL 2013
UNBOUNDING BOUNDED RATIONALITY:
HEURISTICS AS THE LOGIC OF ECONOMIC DISCOVERY
Anna Grandori and Magdalena Cholakova*
ABSTRACT. This paper builds on a long-lasting research program on the
micro-foundations of innovative decision making, founded on a development
of a neglected epistemic aspect of Simon’s work, and on contributions in
epistemology, in which heuristics are not procedures that are uncertaintyavoiding, economizing on cognitive and search effort, and problem-space
reducing, but procedures that are uncertainty-modeling, investing in
research effort, and problem-expanding. The paper offers a summary of the
main effective heuristics of that kind so far identified, as applied to real
processes of innovative decision making under epistemic uncertainty, such
as judging and investing in novel entrepreneurial projects. It argues and
shows that, in contrast to the common view, a wide range of those
procedures, usually thought to belong to different and rival models, can be
fruitfully combined.
INTRODUCTION
This paper builds on a long-lasting research program on the
micro-foundations of innovative decision making (Grandori, 1984,
2010, 2013a). The approach has been founded on an extension of a
neglected side of bounded rationality, present especially in Simon’s
methodological writings (e.g., Simon, 1977) and on insights from
epistemology and philosophy of science in which heuristics are not
necessarily procedures that are uncertainty-avoiding, economizing on
---------------------------* Anna Grandori, Ph.D., is a Professor of Business Organization, Bocconi
University, Milan. Her research and teaching interests include decision and
negotiation analysis, organization design and network research. Magdalena
Cholakova, Ph.D., is an Assistant Professor of Entrepreneurship at the
Rotterdam School of Management, Erasmus University. Her research
interests are in decision making under uncertainty, organizational flexibility
and cognitive structure.
Copyright © 2013 by Pracademics Press
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cognitive and search effort, and problem space reducing, but also
and especially procedures that are uncertainty-modeling, investing in
research effort, and problem-expanding. The latter type of heuristics
has been identified not so much to respond to the question of what
are the common patterns of thinking; but to the question of what are
the best patterns of thinking humans are capable of? (Kiss, 2006;
Lakatos, 1970b). In other terms, the core interest in the latter
approach is the identification of logically sound (hence, in this sense,
rational) procedures for discovery, namely effective and efficient
heuristics that can guide economic discovery, much in the same way
as they do in scientific discovery.
The paper, in its first section, defines the notion of effective
heuristics and the conditions of epistemic uncertainty, which they are
suited to master. In the second section, it offers a summary of the
main effective heuristics for discovery so far identified. They are
illustrated as applied to real processes of innovative decision making
under epistemic uncertainty, such as judging and investing in novel
entrepreneurial projects. The third section addresses the rarely
tackled issue of the complementarity versus rivalry among decision
procedures, arguing and showing through some new empirical
analyses that, in contrast to the common view, decision making, at
least in innovative settings, fruitfully combines a wide range of
procedures, usually thought to belong to different and rival models.
Hence, rather than being a substitute or an alternative to the
traditionally envisaged decision procedures, the identified rational
heuristics enrich the repertory of possible decision behaviors. They
are strategies that are especially useful for understanding and
improving decision making under the strong type of uncertainty
characterizing innovative and knowledge–intensive settings. In
addition, they also help in overcoming some other widely used but
questionable dichotomies in decision making under uncertainty, such
as: compensatory versus non-compensatory strategies intended as
consubstantial with the maximizing versus satisficing opposition (e.g.
Ford et al., 1989); effectuation as opposed to causation strategies
(Sarasvathy, 2001); experience-based as opposed to theory-driven
decision approach (Felin & Zenger, 2009). We instead hypothesize
the possibility (and provide some evidence of it) that on both sides of
those oppositions there are various procedures that can not only be
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assessed as effective for discovery, but can also be effectively
combined.
The conclusions highlight how the new type of heuristics
identified unbound bounded rationality (BR) in several important
respects. While maintaining the main BR point about the importance
of search - i.e., of the generation of decision inputs and not only of
their selection – these heuristics extend the notion of search into one
of research, including rational hypotheses generation and testing. In
the course of doing so, this type of heuristics changes and expands
problem boundaries, hence, in this sense, represents a form of
rationality that is unbounded rather than bounded. Third, the use of
these heuristics allows one to proceed in an intendedly rational way
under a stronger type of uncertainty than the computational
complexity situation typically considered in BR studies. Fourth, the
limits of rationality are, so to speak, shifted to a higher level
altogether, as the logically sound heuristics for discovery are
reconstructed in terms of logical correctness and best observed
patterns of thinking, not of what people commonly really do.
Therefore, finally, the notion of the limits of rationality also changes in
kind: more precisely, the limits to knowledge are distinguished from
the rationality of the strategies by which uncertainty is addressed; i.e.,
we can proceed rationally with partial knowledge of the world.
EFFECTIVE HEURISTICS
A “heuristic” is a method for discovery, in science as in any other
knowing activity (Lakatos, 1970a; Popper, 1989; Kiss, 2006). Per se,
therefore, it involves neither the advantage of a reduction in effort,
nor the disadvantage of a reduction in accuracy or of an increase in
potential biases. In this respect, the advances in BR research after
Simon have been heavily marked by a reduction of the concept of
heuristics to that of shortcuts, with an ensuing focus on the positive
implications in terms of reduced effort and the negative implications
in terms of reduced accuracy and biases (Kahneman, Slovic, &
Tversky, 1982). Some of these include local search, neglect of base
rate information, judging frequency by information availability, overestimating the actor and the self rather than more remote factors in
causal attributions, being over-confident in judgments and so on.
Albeit very useful for proscribing a series of procedures, i.e., for
providing a negative heuristic (Lakatos, 1970a) of decision making,
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this type of research has been less powerful in prescribing what
procedures are to be followed, that is, in providing a positive heuristic
of decision making. In terms of prescriptions, in fact, the prevailing
recommendation has been to conform to the template of statistical
decision theory rules, which, however, in turn, require well-defined,
simplified and structured problems in order to be applicable (Savage,
1954).
Even in strands of studies that have been more concerned with
the positive rather than the negative aspects of heuristics (e.g.,
Gigerenzer & Goldstein, 1996), the advantages have been typically
seen in terms of their efficiency and quickness. In particular, it has
been argued that a variety of fast and frugal heuristics can facilitate
human decision making and allow individuals to make smart
decisions without necessarily having extensive knowledge or
computational skills, or relying on any complex utility maximization
models. Some of the examples of fast and frugal heuristics include
one-reason decision making, ignorance-based decision making, and
elimination heuristics for multiple options choices (see Gigerenzer et
al., 1999). Simon himself, when engaging in the evaluation of
heuristics, largely privileged an evaluation of the efficiency of
alternative search heuristics in recognizing patterns and finding
solutions to problems (Newell & Simon, 1972; Simon 1977).
Some heuristics though - as those used and prescribed in
scientific discovery - may actually involve heavy investments in
research and a deliberate minimization of judgmental biases. They
may be said to be slow and savant rather than fast and frugal, and
are intended to sustain the effectiveness more than the efficiency of
decision processes. But when a heuristic or a method of discovery
can be said to be effective – i.e., to generate high quality solutions,
rather than (or in addition) to be efficient – i.e., to generate solutions
quickly?
In discovery, it is not possible to be always right. More than
minimizing the number of errors, it is important to avoid certain types
of errors and, even more, to deal with errors following correct
procedures, allowing learning from them. For example, it is important
not to make measurement errors, biased inferences, accept false
hypotheses or reject true ones. In other terms, it is important not to
make procedural errors. But falsification, the rejection of false
hypotheses, i.e., the elimination of substantive errors, lies at the very
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core of scientific learning (Popper, 1935). Plausible hypotheses that
turn out to be false are interesting errors, as they let knowledge grow.
Optical illusions, logical mistakes, and wrong inferences are
uninteresting errors, as they could have been corrected even ex-ante
by using sound logics or better observation tools, and they slow down
rather than sustain learning.
It is also impossible, in most situations, to maximize the
probability of being right– and even less to know that whatever has
been found is the maximum or the best achievable – the main reason
being that possibilities are infinite in number and in kind (Popper,
1935). In those unbounded situations, one can say that it is logically
impossible to maximize results or their likelihood in any substantive
sense (choosing the best alternative knowing that it is the best)
(Simon, 1976). However, it is not impossible to maximize results in a
procedural sense, i.e., choosing the superior alternative in a given set
of options (Savage 1954; Sen, 2002). It has in fact been noticed that
maximizing rules of choice can (and should) be separated from
assumptions of complete knowledge (Grandori 2013b). In fact,
whereas complete and infallible knowledge is logically impossible
(Popper, 1935; Russell, 1948; Nagel, 1963), selecting (one of the)
alternatives with maximal results in a given finite set of options is an
entirely possible procedure for choice. In the well-defined conditions
in which it is applicable, it can be said to be superior to other rules of
choice, as any other rule would bring less benefits to the decision
makers.
The main weakness of classic maximizing, defined as a choice
rule applicable in stylized and bounded problems (Savage, 1954), is
not then the most commonly pointed out limitation that it is
unrealistic or inapplicable or involving too much computation. The
real weakness (also pointed out among the classic criticisms raised
by Simon to it) is that it does not include procedures for generating
knowledge about decision inputs. On the other side, the bounded
rationality classic repertory of those generative procedures, or
heuristics, presumes that focusing on search implies not being fully
rational. The present work is based on the thesis that this core
assumption of the bounded rationality tradition is not necessary and
is in need of revision: more precisely that what is needed is a set of
decision procedures that in the course of being heuristic are also
rational or logically correct (Grandori, 2010). This is what we mean by
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effective heuristics: they are not procedures always leading to
selected actions that turn out to be good (with hindsight), but they are
the best procedures available for improving action projects and their
chances of being good. As we shall illustrate, those procedures
acknowledge that the possibilities of action are infinite and therefore
focus more on improving action hypotheses (much in the same way
as hypotheses are improved through research in science), rather than
on comparisons among alternatives (or between alternatives and
some acceptability levels).
The current paper gives an overview of those effective heuristics
as applied to the various logical operations/phases entailed by a
process of innovative economic decision making. It supports and
enriches the argument that they are effective in three ways: a) it
reports empirical evidence that real economic decision processes
under high uncertainty in fact proceed neither by comparing projects
in search of the holy grail of the best project, nor by applying the
typical heuristics of the behavioral repertory, but according to
different procedures; b) it offers theoretical and empirical support to
the thesis that those different, epistemic procedures employed are
indeed effective in constructing knowledge and sustaining innovation;
and c) it addresses, both conceptually and empirically, a rather
untouched issue in decision making research, namely the
complementarity among decision heuristics, conjecturing and
reporting some evidence that most complementarities exists where
mostly
oppositions
have
been
assumed
in
previous
conceptualizations.
EPISTEMIC UNCERTAINTY
An evaluation of heuristics in terms of their logical correctedness
and of their effectiveness in generating high quality decisions, is
particularly relevant when the issue at hand is important, but equally
so, when the type of uncertainty faced is stronger or more profound
than that usually assumed in problem solving research. In most
behavioral research, in fact, problems have been considered as given
and have been actually given to the subjects in simulations and
laboratory experiments. In such contexts, the alternatives that can be
considered are given in kind and uncertainty can stem mainly from
two sources: the sheer number of possible alternatives and the
unpredictable variability of exogenous contingencies (either
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environmental or behavioral). The widely used template of the game
of chess represents this type of uncertainty well. In modern sciences
of complexity these two situations are identified as computational
complexity (Hutchins, 1995) and aleatory uncertainty (Oberkampf,
Helton & Sentz, 2001) respectively. Those two components of
uncertainty put heavy demands especially on the capacity of
processing information of the mind, intended in quantitative terms,
like the memory capacity of a computer.
However, there is a component or type of uncertainty that is
qualitatively different from variability and complexity and asks for
rather different cognitive operations and capacities. It has been often
called Knightian uncertainty in economic sciences (Knight, 1921) and
epistemic uncertainty in complexity theory and natural sciences. It is
a situation in which the relevant alternatives, states of the world and
possible consequences of actions are not given or known.
How can a decision maker proceed under this type of
uncertainty? Economic and behavioral decision approaches are in
surprising agreement on the answer: the higher the uncertainty is, the
less rational the decision maker is bound to be. In economics it is
normally maintained that there is no rational choice with
unforeseeable contingencies (Tirole, 1999). In organizational and
behavioral decision making theory it is normally thought that the less
goals and cause-effect relations are known and clear, the less
decision makers can rely on foresight or even on ex-ante acceptability
judgments, and the more they have to resort to ex-post learning from
experience (e.g., Cohen, March & Olsen, 1976).
What neither of the two main traditional perspectives on decisionmaking considers is that if knowledge is lacking, it can be
constructed, and this can be done rationally. Actually, humans seem
to have quite distinctive capabilities in that activity. The main ways in
which this is done, and even the best ways in which this can be done,
have not gone unanalyzed. On the contrary, they are rather known
from decades of studies on the processes of discovery in science and
in other types of explorative activities (e.g. Lakatos & Musgrave,
1970; Magnani et al., 1999; Hintikka, 2007). Leveraging on those
studies, a new missing set of procedures for epistemic decision
making behavior as a process of rational knowledge construction has
been specified (Grandori, 2010).
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Let’s examine those epistemic heuristics and their role as applied
to the various logical operations/phases entailed by a process of
innovative economic decision making; as well as, next, the relations
among them.
EFFECTIVE HEURISTICS IN THE PROCESS OF DECISION MAKING
From Problem Solving to Problem Modeling
If a problem is not given, a first move in a decision making
process is problem modeling. Modeling has in fact been analyzed as
an alternative, and more effective, learning strategy with respect to
direct and vicarious experience (Bandura, 1986). As Bandura noticed,
modeling is also typically more efficient, since learning by experience
is an extremely slow and error-prone process, sometimes fatal (where
errors are irreversible). In addition past and vicarious experience may
be highly unreliable or absent in the discovery and crafting of new
economic activities (Felin & Zenger, 2009). Model-based reasoning
has in fact been singled out as the general approach to the analysis
and solution of new problems in scientific activities (Magnani et al.,
1999). As in scientific activities though, the point is not to oppose
theory to experimentation. Observation and empirical information is
clearly necessary to test and even to generate good theory; and
conversely, there is no intelligent experience or observation without
reliance upon some conceptual and theoretical lenses (Lakatos &
Musgrave, 1970). It is the way in which empirical evidence is treated
that matters. As we all know, in research there are correct as well as
biasing procedures. The problem is that the type of experience-based
approach envisaged in organizational and behavioral studies is not
close to the canons of correct inference and hypotheses testing.
Rather, it is closer to the principle of reinforcement and to inductive
extrapolation: assuming that what worked in the past or in some
comparable situations should also work in the situation at hand; that
series and trends observed for a certain while should also continue
into the near future, etc. This is closer to logics that are notoriously
fallacious out of very simple and stable contexts (Russell, 1948;
Nisbett & Ross, 1980).
One way to prevent the misuse of experience and empirical
observations, as well as to reconcile theory with observation, is to
apply causal modeling, as opposed to any modeling. In a causal
problem model, alternatives play the role of causes and
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consequences play the role of effects. No decision tree or matrix, or
any partial or complete preference ordering, can be defined without
defining some cause-effect mapping. The quality of hypotheses on
those cause-effect relations is therefore the most important
ingredient for the quality of any choice.
Where do good hypotheses on cause-effect relations come from
however? This question has some history in philosophy of science
and cognitive analysis of discovery processes. The most important
effective hypothesis-generating heuristic is abduction, originally
identified by the philosopher Charles Sanders Pierce, and later widely
used in the methodology of science (Hanson, 1958, Simon, 1977).
Abduction is ‘a logical inference ... having a definite logical form …
:the surprising fact C is observed; but if A were true C would be a
matter of course, hence, there is reason to suspect that A is true’
(Pierce, as quoted in Magnani, 2001, p. 125).
However, as there are different types of explanations, there
are different types of abduction. In particular empirical laws (involving
only observable variables and relations among them) have been
distinguished from theoretical laws (involving unobservable terms
and relations, in particular causal relations) (Nagel, 1961). This
distinction does not imply that there can be any concept-free
observation. Even any observational proposition is a judgment
(hypothesis) that requires acceptance (Popper, 1935). Nevertheless,
an empirical law may be defined a parsimonious recodification of
observations (Simon, 1977); not involving the question of why the
observed regularities or patterns are as they are, hence not involving
causal modeling. Analogously then, a pattern-recognition based
abduction – responding to the question: is there a pattern in
observed data? what is it? – can be distinguished from theoretical
abduction – responding to the question: why is a/that pattern
observed? (Magnani, 2001). The first type of abduction is the only
type of abduction contemplated in the bounded rationality tradition. It
starts from given data, where one only has to recognize patterns and
select plausible general empirical laws from a pre-stored repertory of
possible laws that fit the pattern. For example, observing a series of
prices or of sales, one may recognize an ascending or descending
pattern, and test whether they fit with more refined laws, i.e., linear or
exponential growth (Simon, 1977; Wason, 1960). Theoretical or
causal modeling based abduction goes beyond that, by formulating
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causal hypotheses on why what is observed, or even what has not
been observed yet, should be observed. Hence, theoretical abduction
is stronger, and it may be able to explain/predict phenomena of
which there are no signals in current observations (e.g. the existence
of a never observed planet, or never observed type of firm or
contract). Its logic involves formulating hypotheses on the causal
mechanisms linking the alternatives/causes to the consequences/
effects and testing them, namely developing a theory from which the
observations would follow. As widely acknowledged, though, ‘there
are as many causes of X as there are explanations of X’ (Hanson,
1958:54); hence there is no way to make inferences from
observations to the best explanation (see also Hintikka, 2007). What
is possible, nonetheless, is to generate plausible hypotheses with
higher chances to be corroborated rather than refuted by testing and
justification (Magnani, 2001, p. 19).
Hence, causal modeling and theoretical abduction are stronger
heuristics under epistemic or Knightian uncertainty. Patternrecognizing abduction, instead, while stronger than generating
hypotheses in a blind or random way (Simon, 1977), may be suitable
and sufficient for generating estimates where the relevant variables
are known in kind, although they may be many and vary in value (a
computational complexity and aleatory uncertainty situation). For
example, it is useful in the modeling of seasonal and cyclical
variations of demand for specified products or materials. By contrast,
it would be dangerous in modeling the evolution of demand for
cultural services. In the latter case, a causal model of the evolution of
tastes (as due, say, to increased education, web connection, income,
etc.) would be more reliable.
In addition, pattern-recognizing abduction is insufficient for
formulating hypotheses on which kinds of data to consider in the first
place, or for situations where the question is not to explain
regularities in data (how the world is) but what else can be observed
that we do not observe (the world as it might be). Imagination and
theory are the source of these kinds of hypotheses (Shackle, 1979).
Felin and Zenger (2009) illustrate how economic innovators, such as
entrepreneurs, leverage on experiential and observational fragments
for imagining possibilities through a process of mental experiments in
the generation and justification of entrepreneurial strategies. An
effective illustration used by the authors to support this idea is taken
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from developmental psychology, more specifically with reference to
how infants learn to speak. As has been shown (Chomsky, 1986),
children demonstrate a capacity to “bootstrap knowledge” (p: 133)
much further than what they have actually observed or sensed.
“Ideational trial and error” is also listed as an effective way for
decision-makers to build and test multiple scenarios in their minds
much before taking any action steps, as the costs of errors in this offline learning process is much lower than that of on-line learning
based on real experience (Gavetti & Levinthal, 2000). In studies of
entrepreneurial decision-making, it has been reported that
experienced investors often quiz entrepreneurs over a large set of
hypothetical situations and test how they would respond to these
potential scenarios, in order to get a sense of their ability to engage
with unexpected or unfamiliar paths (Cholakova and Grandori, 2012).
Such prefactual thinking on imagined future scenarios has been
shown to help decision makers in processing a larger amount of
information before formulating a judgment (see Koehler, 1994).
A thick description of how causal and theoretical problem
modeling can sustain economic innovation has been provided in
Grandori (2010). An entrepreneur with background in chemistry, who
engaged in a side activity of olive oil production, learned that one
production problem was to get rid of the industrial waste of olive
water, once separated from oil after pressing. He formulated the
following sets of hypotheses and causal model: if olive oil contained
so many beneficial chemical components (as poliphenoles), so should
the olive water coming from the same source; and those substances
should have been usable as ingredients for healthy dietary and
cosmetic products. He tested the hypothesis having the chemical
composition of olive water analyzed (something that had never been
done). The hypothesis was corroborated, a technology for extracting
poliphenoles from olive water patented, and a new firm using them in
a wide range of products founded.
A further set of important, although seldom analysed, problem
modelling heuristics are procedures for rationally terminating
research. When is a problem model developed enough? How long
should a decision maker go on in search and research?
The search stopping rules traditionally considered in decision
making models, have been based on an explicit or implicit trade-off
between the costs of search and its expected benefits. In a utility
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maximizing approach one should invest in research up to the point
where the marginal costs equal the marginal benefits. In a satisficing
approach, the difficulty in finding is taken as a signal that the
expected benefits of continuing search with the aspiration level in use
are low, so that a lowering of acceptability levels is justified (the
opposite should occur if finding is easy). However, those criteria are
applicable only if problems are well defined, so that those judgments
can be made.
How to choose to terminate research rationally in unbounded
problems? It has been observed that, as much as in other respects,
also in the closing of a research process, guidelines analogous to
those recommended in research methodology can be applied in
decision making (Grandori, 2010). They are based on notions such as
construct validity, measure reliability, and the statistical significance
of relations and correlations. Altogether, these heuristics can provide
an assessment of the acceptability of a problem model. An important
signal that a good problem model has been approximately reached is
its representational stability in spite of further research (Browne &
Pitts, 2004). The relevant good question for constructing a good
problem model then is: when does it become difficult to raise any
further criticism or when do the marginal improvements of those
further criticisms in the problem model and in expected benefits
decline / become negligible?
Such processes and procedures can be found not only in the
evaluation of scientific projects, but also in the evaluation of
entrepreneurial projects. The so called pitch events, in which start-up
projects are presented and subject to questioning, criticism and
discussion by an audience of investors is an example of a structured
and systematic procedure of this type. It is also widely documented
that informal discussions between the project proponents/
entrepreneurs and as wide as possible networks of experts, actual
and possible business partners, consultants and research
institutions, are fundamental in the generation of good new projects
(Burt, 2004; Harrysson, 2006). Of course, the threshold beyond which
further discussion, data gathering and testing is going to bring about
negligible improvements in the validity and robustness of projects is a
discretionary matter, but so it is even in science.
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From Reasoning on Actions to Reasoning on Resources
The above discussion also shows the centrality of questioning
(Magnani et al., 1999; Hintikka, 2007) as a general heuristic to
generate hypotheses in all the phases of decision-making, including
problem modeling. However, in order to generate good hypotheses it
is important to pose good questions. A general theory of good
question-making looks very difficult to achieve. However, at a very
general and abstract level, as already pointed out, causal questions
about the why, are different and more ambitious with respect to
descriptive questions about the what, where and how.
In addition, heuristics for good questioning may be better
specified in a field-specific way. In fact, in the more specified (but still
quite wide, and of central interest here) field of economic innovative
decision making, some heuristics helping in posing good kinds of
question have been identified. In the first place, it has been observed
that causal questions may usefully go in two directions: it can be
asked what the causes of/means to certain observed or desired
effects are; but it can also be asked what effects certain observed or
available causes/means may have. The former is a logic of effects in
search of causes, whereas the latter is a logic of causes in search of
effects.1 Both of these heuristics can be very useful in innovative
decision making, and both require causal hypotheses. This type of
reasoning can also include judgments on the utility of effects, thereby
adding an economic and instrumental criterion to the epistemic
criterion of the validity of causal attribution. Thagard and Croft (1999:
134-35) made their logic explicit in the following way: ‘Why X? Y
would explain X. Then let’s hypothesize Y  X’. Or, in a prescriptive,
utility loaded format: ‘How to do X? Y may produce X. Then let’s do Y
to get X’. Or, proceeding from means in search of ends: “We
observe/have Y. What consequences can Y produce? Which of those
are useful? X and Z are generated by Y and are useful. Then apply Y
to get X and Z”.
The relative incidence of one or the other heuristic should
logically depend on where the higher degrees of freedom lie: if one is
searching for a cure for cancer, one should focus on means as the
sought effect is not easily substitutable with other effects of similar
desirability. In economic discovery, however, there are often higher
degrees of freedom on goals than on means/resources. Goals are not
fixed in content for many types of actors and activities: the main
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purpose of economic activities is often to improve benefits for firms
and customers through any kind of project, good or service. To the
extent that actions are rather free, while resources are more limited
in kind and amount, to use a resources in search for uses heuristic is
particularly effective: it is more likely to generate hypotheses on
actions that improve benefits while not increasing costs.
As applied to economic decision making specifically, the causes
are most often resources, and the effects are most often the
uses/activities that resources can generate. Hence, the two causal
modeling heuristics often take the form of resources in search of
uses and uses in search of resources. The power of the resources in
search of use, therefore, is not that it avoids causal judgment or it
gives more control over the environment, but rather that it reduces
costs and risks; and increases the likelihood of generating viable
hypotheses.
Reasoning on resources is in fact widespread in innovation and
entrepreneurship. The often reported heuristics, used by
entrepreneurs and investors in new firms alike, of betting on the team
or the technology, in the hypothesis that a good stream of projects
are likely to be generated (even if they can poorly be specified exante) is an example. It is like a chef who rather than planning to cook
a specific dish and searching for the ingredients, starts from available
ingredients in his kitchen and asks himself what can be prepared
with them (Sarasvathy, 2001). Other examples include real option
reasoning in entrepreneurial investments (Bowman & Hurry, 1993;
McGrath, 1997) – whereby a resource commitment is done giving the
option to further invest upon testing whether a good project is
actually ensuing.
From the Orderings of Alternatives to the Learning of Interests
Both the groups of heuristics examined so far involve a move
backwards in the causal chain, linking mental constructs as well as
real events: they involve going back from solving a given problem to
defining the problem in the first place; and going back from actions
(and reasoning on actions) to the resources that generated these
actions (and to reasoning on them). This general method, or high
level heuristic, can be fruitfully applied also to the structuring of
objectives and to the shaping of utility functions.
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The dominant way of conceiving preferences is that of a ranking
of alternatives. Surely it is so in economics; but even in organizational
and behavioral views (March, 1997; Kahneman & Tversky, 1979)
preferences are defined to a good extent over actions. In spite of its
diffusion, this way of defining preferences is not particularly
conducive to innovative thinking, especially because it allows
alternatives to drive preferences. In that way, preferences become
highly dependent on how the problem is defined and on the
availability of ex-ante knowledge of what the alternatives are. Where
problems have to be defined and alternatives have to be generated, a
more self-centered definition of utility is justified. In a sense, defining
utility in terms of own aspirations, rather than in terms of rankings of
things, was implicit in Simon’s notion of aspiration levels. However, in
order to define an aspiration level, one should know the relevant
parameters and the level of result sought on each of them. In new
and uncertain domains this is a hard prerequisite to meet. What else
can be done then? There is one field of decision making in which this
issue is widely addressed, namely negotiated decision making.
Negotiation research has been especially instructive in showing that
distinguishing positions from underlying interests is a fundamental
heuristic in effective multi-party decision making (Raiffa, 1982;
Bazerman & Carroll, 1987). Positions have precisely the nature of
preferences expressed over alternatives – i.e., a party prefers a
higher to a lower price, or prefers more time than more money. Asking
why is, again, the core heuristics which may lead to seeing new
solutions (or to seeing a solution at all). For example one may prefer
time to money because of having heavy family duties; and the
provision of child care and house-keeping services may be even more
praised for solving the underlying problem and satisfying the
underlying interests.
In addition to defining objectives in terms of interests rather than
of positions, another type of rational heuristic for defining objectives
has been identified. It has to do with the differentiation and
integration of objectives, the multiplicity and connectedness of
knowledge and interests (Cholakova 2011, 2013; Cholakova and
Grandori 2012; Grandori, 2009, 2013b). It is in fact a well-known
proposition in decision-making based organization theory that
innovation is sustained by the differentiation and integration of the
content of sub-units’ objectives in an organization (Lawrence &
Lorsch, 1967). That principle holds also at the level of individual
UNBOUNDING BOUNDED RATIONALITY: HEURISTICS AS THE LOGIC OF ECONOMIC DISCOVERY
383
decision makers. Cholakova and Grandori (2012) have shown that
decision makers investing in new projects who are able to focus on a
large set of factors when forming a judgment, and at the same time
develop a high level of integration across these factors, are actually
less likely to commit Type I and Type II errors in their judgments (that
is rejecting a good or accepting a bad project).
The multiplicity of objectives in decision under uncertainty is
related and actually ensues from a proper operationalization of
project performance and a proper modeling of its causal predictors.
Asking good questions of the type: what are the possible uses of
resources? and what are the possible effects of action/projects?, and
even what is the capacity of a project to generate high utility
according to the current and the (yet unknown) potential interests of
the current decision maker and of yet unknown possible partners?’
typically generates multipurposedness. Actors may intentionally
design or select projects/actions that have the property of
multifunctionality of effects with respect to many possible interests in
order to increase their robustness under uncertainty (Grandori,
2010).
COMPLEMENTARITIES AMONG HEURISTICS
Previous research on decision making – and on innovative and
entrepreneurial decision-making in particular - has been frequently
framed in terms of opposed or rival models or strategies: maximizing
versus satisficing (Simon, 1955);
compensatory versus noncompensatory strategies (e.g. Ford et al., 1989); effectuation versus
causation (Sarasvathy, 2001), betting on the jockey (the team),
rather than the horse (the project) (Kaplan et al., 2009); exploitation
versus exploration (March, 1997);
experience versus foresight
(March, 1994) and experience versus theory (Felin & Zenger, 2009).
The whole discussion conducted thus far, however, suggests that
those oppositions are philosophically naïve and empirically not so
well grounded. Why not saying that experience nurtures theory, theory
allows observation, the exploitation of resources nurtures the
exploration of their uses, reasoning on effects imply reasoning on
causes, and so on? The approach adopted here then invites to think
that many if not all the effective heuristics identified are not only
compatible but even complementary. After all, on a difficult track, the
more numerous (reliable) the holds are, the better. If theory helps,
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this does not imply that observation hinders. If reasoning on
resources reduces risk, this does not mean that predicting products’
consequences and forecasting demand is detrimental. To use a logic
of project improvement for finding superior action hypotheses does
not mean that acceptability thresholds cannot be used, or that utility
maximizing and compensatory rules cannot be applied to some of the
generated options. To think about how resources can be better
exploited is an important generator of exploration of their possible
uses.
This observation has the further implication of suggesting a more
configurational view of decision making than that usually adopted. A
shift from conceiving behavioral and structural alternatives as
mutually exclusive packages, to conceiving them as combinative
configurations based on the complementarity of elements, is already
taking place and gaining terrain in organization theory (Fiss et al.,
2013). Decision making processes should not represent an
exception, as they are also composite sets of elements, represented
by decision procedures or heuristics. An analysis of their relations of
complementarity, substitutability, compatibility, etc. should be a
fruitful avenue for further research.
A preliminary analysis of the potential interaction among the
above-outlined effective decision making heuristics has been
conducted by Cholakova and Grandori (2012) on a data base of
protocols of the decision processes of expert investors in new
entrepreneurial projects. The protocols included sufficient information
for measuring processes according to the intensity of use of the
following heuristics: resources in search of uses; forecasting
contingencies/states of the world; theoretical and causal modeling;
experience-based modeling and pattern-recognition; multipurposedness and objectives’ integration. In addition, the projects
proposed to investors were selected so as to have independent
information on their goodness: The success of these projects in
generating funding from external investors (not part of the sample)
during actual pitches was used to classify them as good versus bad
opportunities and thus served as an indicator of investors’ error rate.
Therefore, initial empirical evidence relevant for our claim that these
heuristics are a) effective for discovery, and b) complementary, is
provided by exploring the connections between the use of those
heuristics and the chance of committing Type I or Type II errors
UNBOUNDING BOUNDED RATIONALITY: HEURISTICS AS THE LOGIC OF ECONOMIC DISCOVERY
385
(rejecting good projects or accepting bad ones). Among the results we
can signal the following:
- Investors employing resources in search of uses heuristics at
above average levels had a very low error rate, irrespectively of
their level of forecasting. Even though high forecasters had a
lower success rate if compared to high effectuators, the highest
success rates are associated to processes using both kinds of
heuristics – searching for possible uses of resources and
forecasting (albeit at different intensities).
- Multipurposedness (the number of categories that investors
considered during their evaluations) contributed to decision
quality positively, provided that the multiple criteria were used in
an integrated and flexible fashion, either compensatory or non
compensatory (i.e., by performing trade-offs among criteria or by
using them as conjoint constraints, through multiple cycles of reevaluations).
- The application of a larger set of heuristics, in a more thorough
way, per se brought about a more effective project selection. In
fact the overall error rate for investors who scored below average
in heuristic breadth was close to the error rate obtainable by a
random choice of projects, whereas those characterized by an
above average breadth and depth of use of all the considered
heuristics was significantly lower.
CONCLUSIONS
This paper is based on, and offers a summary of, past work
focused on rescuing the notion of heuristics from the prevailing
psychological weak thought view of them, and an analysis of
heuristics as methods for discovery with a focus on evaluating their
effectiveness. The paper reconstructs, illustrates and enriches the
portfolio of
those discovery oriented decision procedures as
necessary to perform in a logically sound way the various logical
operations entailed by processes of innovative economic decision
making. Those heuristics bring about some salient shifts in the logic
of decision making in all the main decision sub-activities: from the
solving of a given problem to problem modeling, from analyzing
actions to analyzing the resources as potentials for generating
actions; and from ranking alternatives according to given preferences
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to learning how interests may be operationalized and even what they
are.
This paper also contributes in un-packing and disentangling
decision models for re-appraising the effectiveness of single
heuristics and of their combinations. The examined heuristics
included: resources in search of uses; forecasting consequences;
theoretical and causal modeling; experience based modeling and
pattern-recognition; degree of multipurposedness and integration in
the structure of objectives. The exploratory data analyses presented
support the claim that those heuristics are both effective and
complementary.
Those effective heuristics may be said to unbound bounded
rationality as they embody a form of rationality that may be qualified
as epistemic (Foley, 1987, Grandori 1984, 2013b) and expandable
(Hatchuel, 2002) or extendable (Secchi, 2011) rather than bounded.
It is epistemic in the sense that it pays fundamental attention to the
validity and reliability of the knowledge used and constructed in the
process – to the search of truths rather than just of benefits – and
because it is the main way we know to proceed rationally under
epistemic uncertainty. And it is extendable and unbounded as it does
not take the boundaries of problems for granted, but treats them as
the main hypothesis to be tested in the process.
The analysis and evaluation of effective heuristics for innovation
conducted here should have immediate implications for improving
decision-making where uncertainty is strong and innovation
important. That situation is certainly of no minor importance, but it is
precisely the condition under which the classic deductive and
maximizing logics fails for lack of requisite ex-ante knowledge and the
behavioral and experiential logics fail for the lack of valid knowledgegenerating procedures. The identified effective heuristics can provide
a missing descriptive and prescriptive model of how decision makers
might and should proceed in judgment under strong uncertainty.
Specifying and evaluating those procedures can also contribute to
clarifying to the decision makers themselves what logics they do and
might use; something they are often unaware of, so that they
attribute successful economic and entrepreneurial discovery to some
special and inscrutable intuition – as it was the case also for
scientists, before the studies the logic of scientific discovery clarified
the underlying rational heuristics.
UNBOUNDING BOUNDED RATIONALITY: HEURISTICS AS THE LOGIC OF ECONOMIC DISCOVERY
387
NOTES
1. The search for ends/effects (of given means/causes) rather than
for means/causes (for given ends/effects) has been called
“effectuation” (Sarasvathy, 2001). We are going to use that term
parsimoniously and only in the narrow sense of a resources in
search of uses heuristic. The construct of effectuation as
commonly used, in fact, has been over-charged with too many
other meanings. In particular, it has been contrasted with
causation, as if the search for effect did not involve causal
judgments; it has been ascribed illusory properties of greater
control over environments, and it has been enlarged to include
choice heuristics that could be easily accommodated in the
traditional satisficing or even classic rational economic model,
such as a criterion of affordable losses.
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