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This essay connects four key ideas from Herbert Simon’s “Sciences of the Artificial” to recent research on entrepreneurial expertise: (1) natural laws constrain but do not dictate our designs; (2) we should seize every opportunity to avoid the use of prediction in design; (3) locality and contingency govern the sciences of the artificial; and, (4) near-decomposability is an essential feature of enduring designs. The essay is based on a series of conversations and emails with Simon about the empirical findings of my doctoral dissertation that involved a protocol analysis study of expert founder-entrepreneurs.
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Entrepreneurship as a science of the artificial
Saras D. Sarasvathy
*
University of Maryland and R.H. Smith School of Business, 3322 Van Munching Hall,
College Park, MD 20742, USA
Abstract
This essay connects four key ideas from Herbert SimonÕs ‘‘Sciences of the Artificial’’ to re-
cent research on entrepreneurial expertise: (1) natural laws constrain but do not dictate our
designs; (2) we should seize every opportunity to avoid the use of prediction in design; (3) lo-
cality and contingency govern the sciences of the artificial; and, (4) near-decomposability is an
essential feature of enduring designs. The essay is based on a series of conversations and emails
with Simon about the empirical findings of my doctoral dissertation that involved a protocol
analysis study of expert founder-entrepreneurs.
2003 Elsevier Science B.V. All rights reserved.
PsycINFO classification: 2340; 3940
JEL classification: D21; D81; L20
Keywords: Entrepreneurship; Rational choice; Effectuation
1. Introduction
Most of us enter the world of research with perceptions of the scholarly life as a
quest for the holy grail. The grand myth of Parsival inspires us even when we might
be less than confident about our own potential to equal his achievement. The majesty
of being a part of the quest itself inspires us... until sooner or later, the terrible fate
of Sisyphus, waking every dawn to push the boulder relentlessly up the mountain,
only to have it drop back at dusk and start all over again in the morning, begins
to loom as possible reality in our research ‘‘careers’’ – and it becomes more and more
difficult to seek meaning and fulfillment in our ‘‘quest.’’
*
Tel.: +1-301-405-9763; fax: +1-301-314-9414.
E-mail address: saras@rhsmith.umd.edu (S.D. Sarasvathy).
0167-4870/03/$ - see front matter 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S0167-4870(02)00203-9
Journal of Economic Psychology 24 (2003) 203–220
www.elsevier.com/locate/joep
I had the good fortune to work with Herb Simon, a Parsival if ever there was one
in the social sciences, from whom I learned that the key was not to strive to become
Parsival, but to ‘‘imagine Sisyphus happy,’’ as Camus compels us in his profound
essay. Many of my meetings with Simon, especially in the early stages of my relation-
ship with him, began by his asking, ‘‘So what do we know today that we did not
know the last time we met?’’ That was not an easy question for a doctoral student
to answer every other week! But I took it as a challenge and began using the opener
to provoke discussions into a wide variety of topics such as: how entrepreneurs
think; the importance of ‘‘naming’’ things in how human beings learn; how embryo-
nic cells know how to become particular types of tissues such as lung tissue or bone
marrow; BorgesÕmazes and LemÕs constructors; and even deeply personal issues such
as what love and death and money meant to him (and to me). Several strands of this
wonderfully messy conversation, however, were converging on the notion of the ‘‘ar-
tificial’’ – the idea of ‘‘design’’ as opposed to ‘‘choice.’’ We had just finished a con-
ference paper based on this particular convergence (Sarasvathy & Simon, 2000), and
were in the middle of revising it for a journal, when the curtain came down on his last
act.
1
This essay is spun from the threads of that conversation. It revolves primarily
around four key themes that Simon outlined in ‘‘Sciences of the Artificial’’ and illus-
trates how they connect together in the world in one particular domain: Entrepre-
neurship. In the following pages, I attempt to tell the story of how we explored
the connections as well as try to establish a thesis about the connections.
The story begins with my empirical investigations of entrepreneurial expertise for
my doctoral dissertation. Based on the findings of this investigation, I developed the
theory of effectuation (Sarasvathy, 2001a) and located its antecedents in KnightÕs
formulation of true uncertainty (Knight, 1921), WeickÕs conceptualization of enact-
ment (Weick, 1979), and MarchÕs technology of foolishness (March, 1982). Only
when Simon and I were invited to contribute a paper to a conference in 2000 and
he suggested that there might be a connection between my formulation of effectua-
tion and his theory of near-decomposability that I began to see the various threads in
the Sciences of the Artificial that directly tied together with effectuation (Simon,
1996).
SimonÕs logic in seeking a connection between the two theories was, as most of his
seminal ideas have always been, very simple: ‘‘As near-decomposability is an astonish-
ingly ubiquitous principle in the architecture of rapidly evolving complex systems, and
effectuation appears to be a preferred decision model with entrepreneurs who have cre-
ated high growth firms, we should be able to link near-decomposability to the processes
these entrepreneurs use to create and grow enduring firms – whether in an experimental
situation or in the real world.’’
Delving into both theories with this new insight led me to realize that the connec-
tion lay in the roles that locality and contingency played in each. Locality here refers
1
When I asked him about death the day after his 80th birthday, he said he could accept that life, like a
play, would have a last act and then the curtains would come down.
204 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
to the fact that cognitive limitations on our rationality allow us to build artifacts that
achieve only local optima at best; yet, our artifacts can endure over time by learning
to adapt to contingencies and sometimes even exploit those contingencies for their
own survival and prosperity. But I am getting ahead of the story here – to tell it
properly, I will first briefly explain (1) effectuation and (2) near-decomposability
and then explore the roles that locality and contingency play in each. If I do my
job right, the story will have the happy ending that effectuation, together with Si-
monÕs work on the artificial can explain the creation of high growth firms; and also,
that several interesting sequels can be developed by envisioning entrepreneurship as
a science of the artificial.
2. Effectuation: A theory of entrepreneurial expertise
For my doctoral dissertation, I used the protocol analysis method that Simon and
his colleagues had used to study experts in a variety of fields (Ericsson & Simon,
1993). My subjects consisted of expert entrepreneurs, founders of firms ranging in
size between $200 million and $6.5 billion and including a variety of entrepreneurial
experiences in a wide variety of industries. Each subject had to solve a 17-page prob-
lem set consisting of 10 typical decision problems that occur in transforming an idea
into a successful firm. The logic underpinning the study was: Given the fact that the
subjects are expert entrepreneurs, and have nothing else in common, is there anything
common in the problem solving processes they use? If so, I could then extract those
commonalties and create a base-line model of entrepreneurial expertise.
The intuition based on extant literature suggested there was no such thing. Several
studies trying to tie psychological traits of entrepreneurs with firm success did not
seem to lead anywhere (see Gartner, 1988, for a good review); entrepreneurs were
also found to range all over the risk-preference spectrum (Brockhaus, 1980; Palich
& Bagby, 1995); and some economists even theorized that entrepreneurial expertise
was nothing but a statistical artifact (see ArrowÕs comments in Sarasvathy, 2000).
But intuition based on extant scholarship is not the only type of intuition avail-
able to us. Another type of experiential intuition has kept entrepreneurship scholars
steadfast in their pursuit of the mythical beast ‘‘entrepreneur.’’ Meeting and talking
with entrepreneurs in person, and interacting with them on a daily basis suggests
there is indeed something that ties them together as a species – something in the lan-
guage they use, the stories they tell, and the way they approach and handle problems
and people. Of course, that could merely be a retrospective retelling of an essentially
random set of experiences. Hence, the protocol analysis to delve into the black boxes
of their cognition.
By the time I got to the 20th entrepreneur in the analysis of the very first problem
(identifying the market for a new product), the coders began to agree that a clear
pattern had come to light about how entrepreneurs create markets and firms. The
key characteristic of this pattern was that it inverted the principles and processes that
we teach students in MBA programs on how they should go about identifying the
market for their ideas. Since these principles and processes are usually based on a
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 205
causal or predictive approach to reasoning, I termed the entrepreneurial approach
‘‘effectuation,’’ to emphasize its aspects of being an ‘‘inverse of causation.’’ To sum-
marize the results quantitatively, 74% of the participants in the study behaved in ac-
cordance with the effectuation model at least 63% of the time, and 44% of them, at
least 85% of the time (Sarasvathy, 2001b).
2.1. Brief outline of effectuation
The model of entrepreneurial expertise extracted from the protocols is complete in
the sense that it identifies a particular problem space, a solution process, a set of
principles and a unifying logic that ties it all together into a coherent whole. Here
I will briefly outline the theory. See Sarasvathy (2001a) for a detailed exposition.
Traditional models based on causal rationality operate in a small, comfortable
clearing in the woods characterized by: (a) given, well-specified goals; (b) well-under-
stood causes and past histories that enable reasonably reliable predictions about the
future; and, (c) an independent environment (such as a ‘‘market’’) that serves to sep-
arate the wheat from the chaff of decisions made by individuals and firms. But all
around this cozy clearing stretches the vast, relatively unexplored jungle where goal
ambiguity, Knightian uncertainty, and endogenous markets dominate the landscape.
This is the problem space for effectuation and is best described by Simon in Sarasv-
athy and Simon (2000): ‘‘Where do we find rationality when the environment does not
independently influence outcomes or even rules of the game (Weick, 1979), the future is
truly unpredictable (Knight, 1921), and the decision maker is unsure of his/her own
preferences (March, 1982)?’’
An empirical example of this Weickian–Marchian–Knightian problem space is the
‘‘suicide quadrant’’ in Fig. 1. Both expert marketers and experienced venture capital-
ists routinely avoid this space that involves introducing a new product in a new mar-
ket. Yet, experienced entrepreneurs know that this is the space within which great
Existing
Market
New
Market
Existing
Product
New
product
Suicide
Quadrant
Fig. 1. Example of problem space for effectuation.
206 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
companies such as EdisonÕs General Electric, Apple Computers and Medtronics often
emerge. The problems in this space, unlike problems involving causal rationality, do
not begin with clearly specified goals.
Some examples should clarify the distinction between the two types of processes.
Imagine the manufacture of a product. In the case of causal or decisional rationality,
the blueprints of the product are provided in advance, together with its costs, and
estimates of market demand; the manufacturer needs simply to procure the raw ma-
terials and process and assemble them according to the predetermined plan. In the
case of effectuation, the manufacturer has a general idea that might lead to a product
that could be marketed profitably. Gillette was looking for something that customers
would have to purchase repeatedly (McKibben, 1998). While he was shaving one
morning, it occurred to him that a non-permanent razor might fit his specification.
He then had to develop a cheap, effective removable-blade razor, generate plans for
creating an adequate initial market, search for sources of funds to get started, and so
on, always modifying his plans as he gained new knowledge from his initial experi-
ments and efforts. This example involves both causal and effectual approaches at dif-
ferent stages of firm creation.
But for the purposes of clear theoretical exposition, a simple, but highly dichoto-
mous example might serve to anchor the arguments better. Imagine the contrast be-
tween a chef to whom a specific menu is presented, and who only has to list the
needed ingredients, shop for them, and then cook the meal (causal decision); and
a chef who happens to find some ingredients in his cupboards, and some utensils
in the kitchen, from which he imagines and produces a delicious meal (effectual de-
sign). In the one case, the givens are assembled, in the other case, they are con-
structed in a constrained environment through imaginative agents in an ebullient
pursuit of interesting (and hopefully valuable) possibilities.
For a more complex example from entrepreneurship, we can contrast the actual
history of an internet company such as RealNetworks (leader in the real time audio
and video streaming industry on the Web) with how we teach entrepreneurship stu-
dents to develop a business plan. If a student came up with the idea of starting an
interactive cable TV channel with progressive content (which was what Rob Glaser,
the founder of RealNetworks originally set out to build in 1994), we would advise
them to proceed as follows: carry out market research to estimate size, growth rate
etc. of key target segments; come up with financial forecasts; write a business plan;
raise funds needed; test market the product and then implement market strategies to
capture as large a market share of the target markets as possible. And the student
would most probably never come upon the idea of giving voice to the ‘‘mute’’
web. In contrast to this, a close examination of the actual history of Progressive Net-
works (later renamed RealNetworks) tells a fascinating story of effectuation with
contingent twists and unpredictable turns, relatively unplanned plunges into proxi-
mate markets, and a relentless endeavor to shape and control the standards in an
emerging industry. See Sarasvathy and Kotha (2001) for a detailed analysis.
Effectuation is not a process of choosing among given alternatives, but of gener-
ating the alternatives themselves, and simultaneously discovering and assessing desir-
able and undesirable qualities of several possible ends. In this sense, effectual
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 207
processes involve design (including the design of alternative goals), not just choice.
Entrepreneurship, involving effectuation, has proved an elusive target for economic
theories, mainly because those theories have, with rare exceptions, been limited to
choices among given alternatives, applying pre-specified criteria, to achieve predeter-
mined goals.
2.2. Means available for effectuation and the solution process
Entrepreneurs begin with three categories of what I have called ‘‘means.’’ They
know who they are, what they know and whom they know – their own traits, tastes
and abilities, the knowledge corridors they are in, and the social networks they are a
part of
2
. Starting with these means, the effectuator asks herself, ‘‘Given who I am,
what I know, and whom I know, what can I do? What types of effects can I create?’’
Contrast this with causal reasoning that focuses on questions such as, ‘‘Given the
particular goal I want to achieve, what ought I to do? Which particular path should
I take?’’ Causal reasoning tends to begin with a universe of all possible alternatives
and seeks to narrow the set of choices to the best, the fastest, the most economical,
the most efficient etc. Effectual processes seek to expand the choice set from a narrow
sliver of highly localized possibilities to increasingly complex and enduring opportu-
nities fabricated in a contingent fashion over time. One important example of this
process, that of entrepreneurial marketing, is represented in Fig. 2.
Causal models of marketing prescribe that the entrepreneur begin with a market
defined as the universe of all possible customers; then divide this universe into rele-
vant segments based on rigorous market research; choose a target segment after an-
alyzing predicted returns and risks for each segment; and finally design marketing
strategies to capture the target market. The effectual model suggests the entrepreneur
should find a customer or a partner searching very locally, just someone from within
their personal social network or through garbage can processes; then generalize the
initial customer or partner into a segment; add segments over time in a contingent
fashion; and eventually define the market for their product/firm.
Causal models are based on a predictive logic: To the extent we can predict the fu-
ture, we can control it. Being able to predict the size, growth rate and potential trends
of target segments, for example, allows the entrepreneurial firm to secure its own
financial future.
2.3. The logic of effectuation
Effectuation suggests a rather different logic for the choice process: To the extent
we can control the future, we do not need to predict it. How does one control an un-
predictable future? The answer to this seemingly paradoxical question lies in the re-
2
At the level of the firm, the corresponding means are its physical resources, human resources, and
organizational resources, a la the resource-based theory of the firm (Barney, 1991). At the level of the
economy, these means become demographics, technological capabilities, and socio-political institutions
(such as property rights).
208 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
alization that a large part of the future actually is a product of human decision mak-
ing. By bringing on board key stakeholders who can ‘‘deliver’’ the future, the entre-
preneur need not waste time, resources, or effort on prediction. Of course, such a
view may express hopes rather than realities, and many entrepreneurs in the real
world do fail. This fact does not negate the hypothesis that they are often more con-
cerned with molding, or even creating, the part of the world with which they are con-
cerned than with predicting it and reacting to the prediction.
Fig. 2. Effectual market creation contrasted with causal marketing.
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 209
This particular logic of control is embodied in three principles that together form
the core of effectual reasoning:
1. Affordable loss rather than expected returns: Causal models focus on maximizing
the potential returns for a decision by selecting optimal strategies. Effectuation
pre-determines how much loss is affordable and focuses on experimenting with
as many strategies as possible with the given limited means. It prefers options that
create more options in the future over those that maximize returns in the present.
The extreme case of this is the zero resources to market principle. This principle
destroys uncertainty by pre-digesting the down side.
2. Partners rather than competitive analyses: Causation models such as the Porter
model in strategy, emphasize detailed competitive analyses. Effectuation empha-
sizes partnerships through pre-commitments from stakeholders as a way to reduce
and/or eliminate uncertainty and erect entry barriers. Pre-commitments from key
stakeholders make uncertainty irrelevant by ‘‘delivering’’ a future that looks very
similar to what was contracted for.
3. Leveraging contingencies rather than avoiding them: When pre-existing knowledge
such as expertise in a particular new technology forms the source of competitive
advantage, causation models might be preferable. Effectuation, however, would
be better at leveraging contingencies that arise unexpectedly over time.
This principle makes uncertainty a friend and an asset, eliminating the need to
overcome it.
Effectuation has at least one major implication for the success/failure of entrepre-
neurial firms. While firms created through effectual processes may not reduce the
probability of failure, they do reduce the costs of failure. They allow failures to hap-
pen earlier and at lower levels of investment, while keeping open the upside option of
making larger investments should early successes begin to cumulate.
That is because the logic of control overcomes the problems of prediction by
keeping investments to the utmost minimum, continually negotiating with key stake-
holders, and learning to use contingencies to create new ends or adapt better to
achieve old ones. The idea of using a logic other than that of prediction is extremely
important for the creation not only of firms, but of any enduring human artifact. As
Simon puts it in his book, Sciences of the Artificial (1996, p. 147): ‘‘Since the conse-
quences of design lie in the future, it would seem that forecasting is an unavoidable
part of every design process. If that is true, it is cause for pessimism about design, for
the record in forecasting even such ‘‘simple’’ variables as population is dismal. If
there is any way to design without forecasts, we should seize on it.’’
3. Entrepreneurship as a science of the artificial
Sciences of the Artificial is one of the most exciting pieces Simon has ever pub-
lished. In an oeuvre of over a thousand publications, that is saying a lot. But it is
also, in my considered opinion, one of the most irritating. It bursts at its seams with
210 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
brilliant ideas and mouth-watering possibilities for scholarship and pedagogy, but
does not develop many of these into something readers can sink their teeth into, es-
pecially in the domains of management and economics. One is left with a sense of the
enormity of work to be done, but not quite sure where to begin. So when we were
invited to write a paper for a technology entrepreneurship conference in 2000, and
Simon suggested in a quietly provocative voice that we should try and link effectu-
ation with the notion of near-decomposability that he had outlined in the book, I
was rather skeptical. But rolling up my sleeves and digging into the fertile loam of
ideas that is Sciences of the Artificial made me realize how close my ideas generated
through my empirical work were to his ideas culled from a lifetime of trying to un-
derstand human artifacts.
3.1. Near-decomposability
3
Near-decomposability is a pervasive feature of the architecture of the complex
systems that we find in the world, both inorganic and organic, ranging from elemen-
tary particles to social systems (Simon, 1969). A complex system is nearly decompos-
able if it is comprised of a number of interconnected subsystems in such a way that
elements within any particular subsystem interact much more vigorously and rapidly
with each other than do elements belonging to different subsystems. There may be a
whole hierarchy of systems, subsystems, sub-subsystems, etc., where this same prop-
erty holds between any two levels. In such systems, (1) the short-term (high-fre-
quency) behavior of each subsystem is approximately independent of the other
subsystems at its level, and (2) in the long run, the (low-frequency) behavior of a sub-
system depends on that of the other components only in an (approximately) aggre-
gate way.
We should note at this point that near-decomposability is not the same as com-
plete decomposability. The key to understanding near-decomposability is that in this
architecture, what constitutes a good design for a component is nearly independent
of the designs of other components. With correlated components, good design in-
volves a space of kn possibilities (nbeing the number of components and kbeing
the number of subassemblies). With complete decomposability, it involves search
in the space of nkpossibilities, and with near-decomposability, it involves approxi-
mation in the space of nkpossibilities. The human body is a good example of a
nearly, but not completely, decomposable system. On the modularity continuum,
nearly decomposable systems are stably balanced between synergistic specificity
and separability (Schilling, 2000).
The structural property of near-decomposability has two implications for the evo-
lution of complex systems:
First: If we begin with a set of simple elements that are capable sometimes of form-
ing stable combinations, and if the combined systems thus formed are themselves
3
In this section, I borrow chunks of my conference paper with Simon (Sarasvathy & Simon, 2000) and
so at least parts of it embody his voice as well as mine.
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 211
capable of combining into still larger systems, then the complex systems we will ob-
serve after this process has proceeded for some time will almost all be nearly decom-
posable systems. The universe as we observe it today provides ample evidence for this
claim. The gradual evolution of the elements from primeval fundamental particles
and hydrogen, then the evolution of successively complex molecules and living organ-
isms – has observably produced nearly decomposable systems with clearly defined
particle, nuclear, and atomic levels, and a whole sequence of molecular levels above
the atomic. Moreover, it has been shown that the time available for evolution of living
organisms on earth is sufficient to produce organisms of the complexity that is actu-
ally observed (say, bacterial complexity) if the organisms and their subsystems are
nearly decomposable, but not if they must be completed by an uninterrupted se-
quence of unions of elementary components (Simon, 1996, p. 189).
And second: If we begin with a population of systems of comparable complexity,
some of them nearly decomposable and some not, but all having similar frequencies
of mutation, the nearly decomposable systems will increase their fitness through evo-
lutionary processes much faster than the remaining systems, and will soon come to
dominate the entire population. Notice that the claim is not that more complex sys-
tems will evolve more rapidly than less complex systems but that, at any level of
complexity, nearly decomposable systems will evolve much faster than systems of
comparable complexity that are not nearly decomposable.
The connection between near-decomposability and rapid evolution is simple and
direct. In nearly decomposable systems, each component can evolve toward greater
fitness with little dependence upon the changes taking place in the details of other
components. Simple mathematics and recent simulations by Marengo, Frenken,
and Valente (1999) have shown that, if and only if these conditions hold, natural se-
lection can take advantage of the random alterations of components with little con-
cern for countervailing cross effects between them. Such a system is like a defective
safe that clicks whenever one of its dials is set correctly, independently of where the
other dials are currently set.
In other words, in nearly decomposable systems failures may be contained as local
events, without disastrous system-wide consequences. Thus nearly decomposable
systems survive not because they make fewer mistakes, but because they can control
the damage locally. Yet, the system as a whole can cumulate the benefits of learning
over time precisely because it is not completely decomposable. It is the tension be-
tween the interdependence of parts and their approximate independence that gives
an evolutionary advantage to nearly decomposable systems. In this particular char-
acteristic, they echo the implications for success and failure suggested by the theory
of effectuation.
3.2. Locality and contingency in near-decomposability and effectuation
In Sciences of the Artificial, Simon shows that the phenomena that constitute the
artificial are imbued with and driven by locality and contingency, both in structure
and movement.
212 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
In designing artifacts, human beings are confined within rather narrow local limits
in terms of space, time and knowledge – primarily because of the bounds on our cog-
nitive capacities and the natural limits on our internal information processing sys-
tem:
First, we can attend only to a limited number of things at a time.
Second, our planning horizons tend to be short run rather than long run.
Third, the stock of knowledge at any given point in time exists dispersed across
individual experts and specialized knowledge corridors that are not always eas-
ily accessible to all decision makers.
Ergo, most artifacts for most purposes are only locally adaptive. In other words,
they survive well only within particular domains and short-run periods during which
the knowledge stock remains relatively unchanged. As Simon observes, ‘‘The world
of economic affairs is replete with local maxima. It is quite easy to devise systems in
which each subsystem is optimally adapted to the other subsystems around it, but in
which the equilibrium is only local, and quite inferior to distant equilibria that can-
not be reached by the up-hill climb of evolution.’’ (1996, p. 47).
Enduring artifacts, however, have to incorporate a way to deal with changes in
local environments over time, whether these are changes in technologies or prefer-
ences or other contingencies that reshape the environment. That is why artifacts that
incorporate the property of near-decomposability in their structure endure better
since they allow parts of the structure to be modified, or even destroyed and rebuilt,
while retaining the rest of the entity relatively unchanged. Nearly decomposable sys-
tems are very good at exploiting both locality (necessitated by the limitations of the
inner environment) and contingency (necessitated by the changing complexities of
the outer environment).
That brings us to the question of how effectual processes can create nearly decom-
posable artifacts. Here the analogy of a patchwork quilt is very useful. Using effec-
tual processes to create firms and markets is somewhat like making a patchwork
quilt. Quilters begin the process with a random assortment of fabric patches and seek
to create a meaningful and pleasing pattern in the quilt they make with them. In the
beginning the quilter could try different combinations of patches that suggest possi-
ble patterns and pictures in the finished quilt. While the availability of the particular
assortment of patches constrains the design, it does not determine it. A good quilter
can create intriguing and even meaningful patterns with the most chaotic of initial
assortments. Furthermore, as the quilt begins to take shape, quilters might seek
out particular patches outside their initial endowments, say from friends and garage
sales. Contingent upon the patches they find, they might change their initial designs
as new possibilities emerge and they imagine better visions for the finished quilt.
It turns out, therefore, that such effectually created patchwork quilts can be rather
good examples of nearly decomposable systems. While particular patches have to
work with other patches to create an interesting pattern, sections can be re-worked
without redoing the entire quilt as the quilt grows larger. A causal analogy to this
effectual quilt would be a jigsaw puzzle, where the picture is already there, and the
pieces are merely to be assembled ‘‘correctly.’’ The patchwork quilt, however, has
no pre-determined pattern and depends almost entirely on the imagination of the
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 213
quilter and his or her mother wit in transforming unexpected contingencies into op-
portunities. In general, while causal models are tethered to goals, effectuation is un-
moored from specific goals enabling the effectuator not only to change particular
goals, but to create multiple new ends that could not have been foreseen at the be-
ginning of the process.
So too the effectual entrepreneur begins with who she is, what she knows, and
whom she knows, to discover at least one customer or partner who is interested in
a product or service she can offer. Thus the first stable configuration of product/
stakeholder/environment comes into existence (perhaps after several aborted starts).
But the first stable configuration changes the means now available to her – her
knowledge corridors expand, her social networks grow larger and even her identity
is enhanced, through reputational and legitimation effects for example. Depending
on who the first stakeholder is and what he or she is interested in, the effectuator
starts expanding the initial configuration and adds new configurations in a contin-
gent (and usually path dependent) fashion. Throughout the process, she seeks to
tie the different pieces together through innovative yet meaningful themes that get
embodied in mission statements, business plans, marketing brochures and press kits.
While the bottoms-up building block by building block process reduces costs of fail-
ure, the continual effort to create a unified identity allows successes to cumulate,
learning to occur and competitive competencies to be forged.
In this way, effectuation too creates nearly decomposable artifacts. Firms cannot
be completely decomposable or 100% modular, if they are to have a strong identity
that inspires loyalty and trust with internal stakeholders. Yet, they need to be some-
what decomposable, so negative feedback from a variety of stakeholders can be in-
corporated to re-work parts of the firm as it grows and endures in the marketplace. It
is this particular opportunity to perceive and harness advantages both from the in-
terdependence of parts and their independence that gives effectually created nearly
decomposable entities a peculiar edge in evolving faster and enduring longer.
In the spirit of one of SimonÕs favorite storytellers, Borges (1980, p. 107), who
said, ‘‘IÕve observed that people tend to prefer the personal to the general, the con-
crete to the abstract,’’ I will now provide an extract from one of the protocols in my
study. The extract lucidly illustrates how effectuation stitches together nearly decom-
posable firms. I use the extract, not as evidence for the existence of effectual pro-
cesses, but merely as an illustration of how they may build near-decomposability
into economic artifacts. Here the subject has been asked his opinion as to the growth
possibilities for an imaginary firm that begins with a single imaginary product, a sim-
ulation game of entrepreneurship. Notice how he begins by not showing much faith
in the product, but gradually imagines himself into the vision of a great company
(see phrases in bold font). Notice also that at least thrice during the protocol he
strives to tie together the different bits and pieces he is imagining through a common
theme or an ‘‘identity’’ of sorts (see italicized phrases).
‘‘This company could make a few people very rich, but it cannot... I dont
think it could ever be a huge company. The basic concept is a business sim-
ulator... startup simulator... so... in the same way in a jet simulator you
214 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
can hop in and fly something electronically and not blow it up... so you
can hop into a business situation and practice and get a lot of reflexes
built up and thought processes built up up front. So... a successful
launch of the first product with a big marketing sales push to penetrate
as many different markets as we could... might have a successful second
product. For example, you could have a product which is how to succeed,
prosper, grow and get promoted within a large company. Making an
equivalent product for the quote organization person as opposed to the
entrepreneur would give you market of everybody with aspirations at
IBM, AT&T, Exxon etc. etc. so... That product could be a follow-on
product...the research would be similar, the product development would
be similar, and so the production part would be equivalent and some of
the same marketing channels would also work. You could make another
product, would be, for students. How do I graduate in the top 10% of
your class at Stanford, or Harvard or Yale. And there... you could sim-
ulate the learning process in the classroom. and research traits that tend
to make you successful or not. study habits that tend to make you suc-
cessful or not. and... a lot of how to be a good student is teachable. A
lot. In my case for example, I took – So there are studying habits that
IÕm aware of and you can do research on successful students and you
could develop a profile that the... marketing pitch of which should
be... students who graduate in the top 10% of a college class arenÕt just
smart in an accident. They have different habits and ways of doing busi-
ness that cause them to be successful and those are neither genetic nor in-
telligence related... they are learnable. So thereÕs your... now you got a
product that can... you can sell to every student in the country. uhm...
so we talked about entrepreneur business, big business, students, so were
really talking about any learning in an interactive situation where simula-
tion is a benefit. So you got... next there is negotiation... there are books
on negotiators... how to negotiate... famous books... here you could...
in reading a book about negotiation would be less effective than having
an interactive 3D game about negotiation. So there you could practice
being a good negotiator. And that would work. ThereÕs not a salesman
in the United States who wouldnÕt buy one of those. How to sell you
know so you got you know another learning situation where how you
act and how you push people can can help you sell better. so... there
is sales. So I guess you could go on and on and then you could generalize
the thing to any situation which requires some sort of technical knowl-
edge... technical knowledge of negotiating... technical knowledge of
bio-molecules... which also involves human organization... people you
have to deal with... both outside the company to get them to help...
to work with them and inside the company to get them to understand
what is the companyÕs methods objectives etc. So an organization in a
learning situation with technical requirements. That simulation that had
those traits so now you can... I gave four five endeavors... you can
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 215
expand that so... maybe IÕm gonna change my opinion about the growth
potential for the company... The company could.. it is easy to see how
within an hour you could name ten products and the ten products would
address huge markets like all employees in Fortune 500 companies that..
who are rich enough to pay hundred dollars for it. So now all of a sudden
you can see itÕs a software that could be a... could be a hit on the scale of
Lotus.. what Lotus was to the spreadsheet world. And therefore you could
see a several hundred million dollar company coming from it.’’
To summarize the exposition so far, both effectuation and near-decomposability
exploit locality and contingency in the evolution of the artifact. Just as effectuation
creates rapidly evolving artifacts that leverage the interdependence of parts to exploit
locality and contingency, so near-decomposability in the structure of such systems
leverages independence of parts to exploit the same locality and contingency. While
effectuation stitches together pieces of entrepreneurial fabric into economic quilts
that continue to make sense in an interactive and dynamically changing environment,
near-decomposability identifies lines of ‘‘tearing’’ so that pieces can be re-worked
in synchrony with the overall pattern as the needs imposed by the environment
change.
Together they can provide a convincing explanation for the creation and growth
of the firms that we see in the real world. One way to substantiate such an explana-
tion would be to analyze the historical evidence already available to us. For example:
Wedgwood Pottery (Koehn, 1997), General Electric (Baldwin, 1995), U-Haul (Silver,
1985) and AES Corp (Waterman, 1990) all contain evidence as to how effectuation
processes have built large and rapid-growth firms with built-in near-decomposabil-
ity in their organizational structures. More general histories of the spread of ‘‘divi-
sional’’ architectures through American industry can be found in Drucker (1947)
and Chandler (1962). Today, we can see numerous new examples of companies that
grow through franchising, joint ventures, and more recently, through ‘‘affiliate’’ pro-
grams pioneered by internet companies such as Amazon.com.
3.3. A vision for the effectual artifact
My research had already shown that entrepreneurs (rightly or wrongly) did set out
to design firms and to a considerable extent, even markets for their firms through the
logic of control. In this sense, entrepreneurship is a science of the artificial. But as
Simon points out in his book (1996, p. 113), ‘‘The previous chapters have shown that
a science of artificial phenomena is always in imminent danger of dissolving and van-
ishing. The peculiar properties of the artifact lie on the thin interface between the
natural laws within it and the natural laws without. What can we say about it? What
is there to study besides the boundary sciences – those that govern the means and the
task environment?’’
He then goes on to explain what the contents of a science of the artificial might
consist of, ‘‘The artificial world is centered precisely on this interface between the in-
ner and outer environments; it is concerned with attaining goals by adapting the for-
216 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
mer to the latter. The proper study of those who are concerned with the artificial is
the way in which that adaptation of means to environments is brought about – and
central to that is the process of design itself.’’ It is here that entrepreneurship builds
on SimonÕs formulation of a science of the artificial and moves it toward new hori-
zons. I would like to argue that the design of entrepreneurial firms in general might
involve something more than adaptation of the inner environment to the outer; it
might involve negotiation between the two. That is because, more often than not,
the environments of entrepreneurial firms (as well as markets in general) consist of
the contingent decisions of other human beings.
Without belaboring the point too much, I am not alone in my thesis that markets
are not ‘‘natural phenomena’’ based upon economic inevitability or even human ne-
cessity. However much economists might argue that de gustibus non disputandum est,
there is considerable historical and other types of evidence that marketers and entre-
preneurs do succeed in their efforts to shape the preferences and tastes of their cus-
tomers. As early as 1939, Schumpeter pointed out, ‘‘It was not enough to produce
satisfactory soap, it was also necessary to induce people to wash’’ (Schumpeter,
1939, p. 243). More recently, Carpenter and Nakamoto (1989) theorize based on em-
pirical data that the practice of branding is essentially the formation of new prefer-
ence structures in the psyches of consumers. In a recent book, Koehn (2001)
chronicles several entrepreneurs from Wedgwood to Dell who created highly success-
ful enduring brands. Other economists too have argued against our assumptions of
markets as something exogenous to the economic process, or something to be as-
sumed as a ‘‘given’’ in our analyses. Olson and Kahkonen (2000, p. 1) put it as fol-
lows, ‘‘The fourth primitive of economic thought – and of most lay thinking on
economics – is so elemental and natural that it is usually not even stated explicitly
or introduced as an axiom in formal theorizing. It is the half-conscious assumption
that markets are natural entities that emerge spontaneously, not artificial contri-
vances or creatures of governments.’’ Finally, Arrow (1974, p. 8) admits, ‘‘Although
we are not usually explicit about it, we really postulate that when a market could be
created, it would be.’’
Therefore, if we do not take markets as completely exogenous to the economic
process, and view them instead as preferences being formulated and decisions being
made by a set of human beings that can be influenced by the actions of the entrepre-
neur, the effectual artifact of entrepreneurship (e.g. the firm) does not just adapt to
its external environment (‘‘the market’’). Instead it has the option of negotiating with
its environment, to shape the environment at least partially in its own image, just as
it adapts other aspects of its internal self to effectively reflect the environment.
One might of course argue that negotiations too are a form of adaptation. That
would raise the question, ‘‘Adaptation to what?’’ Effectuation is fundamentally dif-
ferent from other forms of adaptation such as those involved in biological evolution.
While the effectuator does adapt to changing circumstances outside his control, he
also actively seeks to reshape his environment through those parameters that do sub-
mit to his control. Effectuation, therefore, includes adaptive techniques such as im-
provization, socio-psychological techniques such as bracketing and enactment, and
aggressively effectual techniques of negotiation such as lobbying the government,
S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220 217
participating in standards bodies, and obtaining pre-commitments from influential
stakeholders etc.
Understanding the role of such negotiations between inner and outer environ-
ment, whether as part of an adaptive process or in parallel to it, should form one
of the core areas for research in entrepreneurship. Another key area for research sug-
gested by the formulation of entrepreneurship as a science of the artificial consists in
the role of firm failures in allowing entrepreneurs to understand limiting properties
of the artifacts they create in relation to the environments with which or within
which they negotiate.
In sum, the theory of effectuation suggests that entrepreneurship is indeed a sci-
ence of the artificial and that it builds on at least four key ideas in Sciences of the
Artificial:
1. Natural laws constrain but do not dictate our designs – i.e., within the constraints
of natural law, our designs are contingent on our imagination; there is nothing
intrinsically ‘‘inevitable’’ about them. Implication for entrepreneurship: Given
who we are, what we know, and whom we know, we can build a variety of effec-
tual artifacts by focusing on what we can do, rather than continually worrying
about what we ought to do, given pre-determined goals.
2. We should seize every opportunity to avoid the use of prediction in design. Impli-
cation for entrepreneurship: Designing without final goals allows us to free our-
selves from the pitfalls of prediction so we can use other mechanisms such as
the scientific method, or the garbage can (Cohen, March, & Olsen, 1972), or
the effectual logic of control.
3. Locality and contingency govern the sciences of the artificial. Implication for en-
trepreneurship: Contingencies can be viewed as opportunities to be exploited
rather than as misfortunes to be avoided; while successes and failures are always
local, cumulative learning is still possible.
4. Near-decomposability is an essential feature of enduring designs. Implication for
entrepreneurship: Effectual processes that exploit locality and contingency through
both interdependence and independence of parts are more likely to result in en-
during firms.
4. Conclusion
When I came up with the first draft of the conference paper, Simon had some dif-
ficulties with my cooking and quilting metaphors. He was used to watchmakers and
clicking safes. But eventually as we talked and emailed back and forth, and partic-
ularly when we started discussing the role of locality and contingency in the two the-
ories, he began to see that the quilting example was particularly apt for what we were
trying to establish in the paper and admitted to me in an email that he was ‘‘more
persuaded than before of the effectiveness of the cookery and quilting metaphors.’
It was one of those treasured moments when I was able to surprise him in answer
to his familiar question, ‘‘So what do we know now that we did not know the last
218 S.D. Sarasvathy / Journal of Economic Psychology 24 (2003) 203–220
time we met?’’ He will always live in that question for me, and I am filled with grat-
itude I got to explore it with him so many times. Rest assured, Herb, I will keep try-
ing to catch you by surprise one of these days.
Acknowledgements
I thank Mie Augier for inviting me to write this essay and being patient with my
tardiness on delivery. I thank Jim March for his conversation and encouragement in
several of my scholarly efforts. I am grateful to Anil Menon for his comments and
feedback on the paper and particularly for suggesting the relevance of the myths
of Parsival and Sisyphus for scholarly work. I thank Martin Schultz and Rob Wilt-
bank for comments on an earlier version of this paper.
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... The development of opportunities involves the concept of artefacts (Sarasvathy, 2003;Venkataraman et al., 2012Venkataraman et al., , 2013. Opportunities can be portrayed as artefacts arising from the actions and interactions of entrepreneurs (Venkataraman et al., 2012). ...
... A study of entrepreneurial artefacts show different mechanisms of developing entrepreneurial artefacts (Venkataraman et al., 2012), such as bricolage (Baker and Nelson, 2005), effectuation (Sarasvathy, 2001), pattern recognition (Baron and Ensley, 2006), and transformation in the case of creating new markets (Dew et al., 2011). The science of artificial theory (Simon, 1996) views entrepreneurship as a science of the artificial (Sarasvathy, 2003;Venkataraman et al., 2012), where artefacts are described as 'objects and phenomena in which human purpose as well as natural law are embodied' (Simon, 1996, p. 3). A more practical description of human artefacts is as follows: ...
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Thesis
Start-ups have gained media attention since Google, Facebook and Amazon were launched in the 1990s. The book Lean Start-up, published in 2011, was another important milestone for digital start-up literature. As unicorn companies emerge around the world, topics highlighted in the news include the vast amount of capital that digital start-ups are raising, the ways in which these digital ventures are disrupting industries, and their global impact on digital economy. However, digital start-ups, digital venture ideas, and their venture creation process lack a unified venture creation model, as there is a gap in the re- search on entrepreneurial processes in a digital context. This research is an explorative study of the venture creation process of innovative digital start-ups that examines what is missing from entrepreneurial process models in a digital technology context and investi- gates how early stage digital start-ups conduct the venture creation process, starting with the pre-phase of antecedents and ending with the launch and scaling of the venture. The research proposes a novel process model of innovative digital start-up venture crea- tion and describes the nature and patterns of the process. A conceptual model was devel- oped based on the entrepreneurship, information systems, and digital innovation litera- ture and empirically assessed with a multi-method qualitative research design. The data collected from semi-structured interviews, internet sources, and observation field notes covered 34 innovative digital start-ups and their founders. Interviews were conducted in- ternationally in high-ranking start-up ecosystems, and the data were analysed with the- matic analysis and fact-checked by triangulating internet data sources. The contribution to entrepreneurship theory is a new illustrative model of the venture creation process of innovative digital start-ups, including the emergent outcome of the process having a digi- tal artefact at its core (e.g., mobile apps, web-based solutions, digital platforms, software solutions, and digital ecosystems). Digital platforms and their multiple roles in the process are presented, as well as the role of critical events as moderators of the process which trigger new development cycles. During the venture creation process, the recombining of digital technologies, modules, and components enabled by digital infrastructures, plat- forms, and ecosystem partners represent digital technology affordances. This recombina- tion provides opportunities for asset-free development of digital venture ideas.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
Thesis
Full-text available
Much of the research on effectuation to date has focused on the effectiveness of this entrepreneurship theory in different contexts and its performance relative to other theories. This work aims to create a framework that enables simulation- based studies of effectuation and at the same time lays the foundation for the development of start-up supportive decision-making systems. To this end, the extent to which effectual learning can be modelled and algorithmically interpreted is discussed. Existing simulation models that describe effectuation are first vali- dated, verified and compared. Based on this, an aggregated model is developed with the help of methods of agent-based modelling and reinforcement learning that enables effectual behaviour in the context of a prototypical entrepreneurial situation. The results show that an entrepreneurial agent is able to learn effectual behaviour. Differences in performance during learning occur when the environ- ment changes. The success of the agent depends on the commitment of potential partners or customers. Furthermore, learning success can be determined if the agent applies the affordable loss principle in conjunction with market-conform behaviour. In the future, the developed model can be used to conduct further studies on effectual learning behaviour, taking into account the decision-making behaviour of a real entrepreneur.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
Chapter
Full-text available
Zusammenfassung Die Auseinandersetzung mit bestehenden Konzepten zur Modellierung von Effectuation dient einerseits einer Annäherung an die Forschungsfrage und andererseits der methodischen Aufarbeitung der Ansätze. Die in diesem Kapitel behandelten Simulationsmodelle werden zunächst beschreibend dargestellt und deren Wirkungsweisen im Kontext des realen entrepreneurialen Phänomens rläutert. Darauf aufbauend erfolgen die Verifikation sowie mathematische Formalisierung und Modellierung. Anschließend werden die Ergebnisse der bestehenden Studien kritisch evaluiert und mit den aus replizierten Implementierungen gewonnenen Ergebnissen verglichen.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
Chapter
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Zusammenfassung Effectuation erweitert das Gebiet entscheidungstheoretischer Konzepte und versucht sich von bisherigen Ansätzen, wie beispielsweise dem bayesschen Schließen, abzugrenzen. Im Rahmen des Kapitels wird Sarasvathy’s Interpretation des bayesschen Wahrscheinlichkeitsbegriffs unter Zuhilfenahme der Bildung eines Wahrscheinlichkeitsraumes mathematisch formalisiert. Darüber hinaus werden Ansätze des maschinellen Lernens im Kontext von Effectuation erarbeitet, die über die bisher in der Literatur zu findenden Ausführungen hinausgehen. Sie stellen die Grundlage für das in der Arbeit entwickelte effektuative Entscheidungsmodell dar.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
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Zusammenfassung In diesem Kapitel werden die wesentlichen Erkenntnisse und Grenzen der Arbeit herausgestellt und weitere Forschungspotentiale aufgezeigt. Durch die Verwendung von Reinforcement Learning und agentenbasierter Modellierungsmethoden wurde die Operationalisierung des entrepreneurialen Problemraums realisiert und effektuatives Lernen ermöglicht. Das realweltliche Phänomen einer Unternehmensgründung konnte aus Gründen der Komplexitätsreduzierung im entwickelten Modellansatz nur unter Einschränkungen erfasst werden. Weitere Forschungspotentiale ergeben sich hinsichtlich der Evaluierung weiterer Lernstrategien, modifizierter Belohnungsfunktionen sowie der Anpassung der Lernumgebung des Agenten.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
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Zusammenfassung Das Lernverhalten des effektuativen Agenten wird bei Veränderung der Dynamiken der Lernumgebung sowie verschiedener Parameter untersucht. Es werden Leistungsvergleiche bei Manipulation der Belohnungsfunktion, Variation der Transitionswahrscheinlichkeiten sowie ausgewählter Hyperparameter angestellt. Die Ergebnisse zeigen, dass ein Agent grundsätzlich in der Lage ist effektuative Prinzipien zu erlernen. Es werden Parameter- und Umgebungskonstellationen eruiert, die effektuatives Lernen begünstigen.
... Im Gegensatz dazu bedeutet kausale Logik im Kontext unternehmerischer Tätigkeit, Ziele festzulegen und diese mit Hilfe zu beschaffender Ressourcen und zu definierender Aktivitäten bestmöglich zu erreichen. Die Entscheidungsfindung findet unter Zuhilfenahme von Vorhersagen statt (Sarasvathy, 2003). Effectuation hingegen setzt bei den vorhandenen Kompetenzen und bereits bestehenden Kontaktnetzwerken des Individuums an, die als Grundlage für das weitere Vorgehen dienen. ...
... Konkret repräsentiert M t das aktuell vorhandene finanzielle Budget des Agenten, welches ihm die Anpassung des Produktes E ermöglicht. Durch Einbeziehung der Mittel M in die Modellierung werden die vonMauer et al. (2017) undWelter und Kim (2018) definierten Problemräume erweitert und der vonSarasvathy (2001),Sarasvathy (2003),Wiltbank et al. (2006) und Zhang und Van Burg (2019) beschriebenen means, als Entscheidungsgrundlage im effektuativen Prozess, Rechnung getragen. Im von KfW Research (2017) publizierten Gründungsreport wurde herausgestellt, dass Existenzgründer im Jahr 2016 mit einem mittleren Eigenkapitaleinsatz von 7.500 e eine Unternehmung begonnen haben. ...
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Zusammenfassung Zur algorithmischen Interpretation von Effectuation kommen Ansätze agentenbasierter Modellierung zum Einsatz. Zur Abbildung effektuativen Lernens werden Methoden des Reinforcement Learnings zur Anwendung gebracht. Anhand einer protypischen Gründungssituation wird ein Zustandsraum gebildet, der als Lernumgebung des effektuativen Agenten dient. Durch Modellierung einer Belohnungsfunktion erhält der entrepreneuriale Agent die Möglichkeit Effectuation zu erlernen. Die Verwendung des Q-Learning-Algorithmus als Lernstrategie erlaubt die Modellierung der Kernelemente des entrepreneurialen Problemraums.
... In those environments, adaptive strategies for control are more appropriate (Mintzberg & Waters, 1985). By and large, uncertainty in entrepreneurial activities is more the rule than the exception, so approaches that lead to non-predictive control have been proposed (Sarasvathy, 2003;Wiltbank et al., 2009). These nonpredictive control strategies deal with shaping and reshaping the environment by stakeholder inclusion and participation, instead of conceiving the market as an external object whose trends can be forecasted with high accuracy. ...
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