=Paper=
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|pdfUrl=https://ceur-ws.org/Vol-766/paper22.pdf
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|dblpUrl=https://dblp.org/rec/conf/istar/SchlueterSBJK11
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https://ceur-ws.org/Vol-766/paper22.pdf
CEUR Proceedings of the 5th International i* Workshop (iStar 2011)
Causal vs. Effectual Behavior –
Support for Entrepreneurs
Jan Schlüter1 , Dominik Schmitz2 ,
Malte Brettel1 , Matthias Jarke2 , and Ralf Klamma2
1
RWTH Aachen University, Lehrstuhl Wirtschaftswissenschaften für Ingenieure und
Naturwissenschaftler, Templergraben 64, 52056 Aachen, Germany
{schlueter,brettel}@win.rwth-aachen.de
2
RWTH Aachen University, Informatik 5, Ahornstr. 55, 52056 Aachen, Germany
{schmitz,jarke,klamma}@dbis.rwth-aachen.de
Abstract. “Effectuation” is a new approach to explain the success or
failure of entrepreneurs. In contrast to the traditional “causation” ap-
proach the entrepreneur is not considered to be driven by a concrete
goal and to choose between different alternatives in regard to how well
they help to achieve this goal. Instead the entrepreneur evaluates the
alternatives, in particular the choice of strategic partners, in regard to
their potential for future success. The goals are adapted to the choices
and in particular the needs of the strategic partners. Agent-based simu-
lations are intended to help identifying the settings where one approach
is more appropriate than the other.
1 Introduction
The IMP Boost project “Overcoming Barriers in the Innovation Process” inves-
tigates a new approach to explain the success or failure of entrepreneurs. At the
center of interest is the notion of “effectuation” (http://www.effectuation.org) [6,
7]. This denotes a fundamentally different way to act in comparison to traditional
approaches in economics, now denoted by “causation”. A “causal” entrepreneur
starts by carrying out comprehensive (and rather expensive) market studies to
clearly identify a dedicated market opportunity. This is then settled as a goal and
the entrepreneur only decides between different alternatives in regard to their
utility to achieve the settled goal. In contrast to this an “effectual” entrepreneur
is not committed to a particular product or goal, but only to the desire to run
an enterprise. Instead of carrying out expensive market studies she chooses from
alternatives in regard to the resulting opportunities and under consideration of
the “affordable loss”, i. e. how much money she can loose without harming her
capacity to act. A major means of an “effectual” actor is to utilize her knowledge
and network to find cooperation partners. These can be potential customers as
well as money donators. Very much in contrast to the “causal” approach these
strategic partners can have a great influence on the actual product or goal to
be achieved. This is the “effectual” entrepreneur simply adapts the goal to the
partner’s needs, including the chance to build a completely different product.
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CEUR Proceedings of the 5th International i* Workshop (iStar 2011)
The reward to this flexibility is a definite commitment of the partner to become
a part of the new venture. Thus, the two approaches differ considerably in re-
gard to how they address uncertainty. While the “causation” approach employs
prediction to reduce uncertainty, “effectuation” controls the unknown future by
taking decisions, i. e. explicitly deciding which way to go.
2 Objectives of the Research
The aim of the IMP Boost project is to compare the two approaches – causa-
tion vs. effectuation – by running simulations [8]. Based on theoretical research
neither of the two approaches is to be favored in general. Accordingly, we need
to identify the settings, conditions, and constraints that put either of these ap-
proaches in front.
From first modeling experiences and foundational considerations, agent-based
approaches toward modeling and simulation seem to be well suited. To deepen
the basic understanding of the two processes, we have first gone for a qualitatively
oriented modeling with the help of the i* framework. This approach builds on
successful earlier experiences with modeling and simulating entrepreneurial net-
works with i* [1–4]. Afterward for simulation purposes, we want to go beyond
the qualitative logic-based simulations proposed in the earlier investigations,
by considering quantitative simulations via the Repast agent-based simulation
framework (http://repast.sourceforge.net). This allows for a more thorough in-
vestigation of a larger variety of parameter settings.
3 Scientific Contributions
3.1 A Process Model for “Causation”
Figure 1 shows a preliminary and partial modeling of a “causation” based ap-
proach toward venture creation (based on [5]). The main actors are the “en-
trepreneur”, a “market research institute”, and “resource providers”, in most
cases venture capitalists or business angels. As it becomes obvious from Fig. 1,
the goal-orientation of the “causation” approach fits well with the traditional
understanding and modeling of agents with beliefs, desires, and intentions. The
only extension compared to vanilla i* is the consideration of sequence links and
a more general precondition/effect element (graphically depicted by a triangle)
as introduced in [1].
3.2 A Process Model for “Effectuation”
In contrast to this the modeling of “effectual” behavior runs into some problems
(see Fig. 2, again based on [5]). The “effectual” approach is to be considered goal-
oriented only in a very generic way, such as “create a venture”. The concrete
business idea, if formulated as a goal, needs to be alterable over time. Further on,
while the “causation” model is mainly sequential, effectuation inherently asks
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CEUR Proceedings of the 5th International i* Workshop (iStar 2011)
Fig. 1. Preliminary i* Model for “Causation”
Fig. 2. Preliminary i* Model for “Effectuation”
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for loops. The new partners that are sought and from whom commitments are
collected can change either the means – i. e. what is possible – or the goals – i. e.
what the entrepreneur (concretely) strives for – at a product/widget level. Pre-
existing contacts are visited one after the other and each can trigger the same
processes on negotiating commitments and/or adapting products. Thus, not only
the concrete goals can change dynamically. In just the same way, also more
and more means become available, possibly after any new potential partners is
contacted.
While there have been proposals to embed control loops into the i* modeling
language (see, for example, [10]), they focus the SR level, in particular decompo-
sitions. Thus, it has to be investigated whether these extensions are also suitable
to capture repeated interactions between agents as occurring in this setting. Al-
ternatively, a more strategic, high-level annotation of dependencies may be con-
sidered. Yet the more operationalized “control flow” considerations mentioned
above might prove valuable when addressing the model-based transformation
toward quantitative agent-based simulations.
3.3 Logic-Based Simulations
By referring earlier work on entrepreneurship networks [1–4] we plan to analyze
“causation” and “effectuation” with the help of qualitative, high-level logic-based
simulations. For this purpose we want to make use of an established model-based
mapping from i* to ConGolog, a logic-based simulation framework enabling
concurrency. The particular process models in i* as preliminarily proposed above
are the first steps in this regard. Yet, we still have to investigate in how far the
simulation framework and the existing transformation need to be reconsidered
once departing from the focus on networks and trust issues therein.
3.4 Agent-Based Simulations
In regard to quantitative agent-based simulations first steps have been taken
by manually implementing a simplified agent-based simulation model on top of
the Repast agent-based simulation framework (http://repast.sourceforge.net/).
The model has four key components: (1) product ideas – a feature vector with
n elements, (2) three types of agents – causators, effectuators, and consumers,
(3) market demand – demand vectors of all agents, and (4) a pay-off landscape
to measure performance via market fit. To ease comparison, there are pairs of
causator and effectuator agents with identical product ideas. During the sim-
ulations, the causator and effectuator agents then finalize their product ideas
by filling the remaining flexible features. The causator uses a market study for
this purpose while the effectuator relies on her network and the sequential ne-
gotiation of commitments with a rather small number of stakeholders that are
consulted.
While this simple model does not yet have all the necessary features – for
example, the principle of “affordable loss” is not yet considered –, it has already
confirmed the basic theoretical results, for example, the role of uncertainty. But
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at the same time the simulations have delivered more insights on the fine-granular
details of the considered situations. In particular, they have revealed the need
to consider the influence of market concentration and fragmentation on the su-
periority of either approach. For more details see [8].
4 Conclusions
The first findings of agent-based simulations are promising in that they confirm
that the theory of “effectuation” is plausible. In particular, the important role of
uncertainty is acknowledged. On the other hand, the current simple simulations
do not correctly reflect all relevant features such as “affordable loss” or a more
detailed investigation of the concerned network of contacts let alone its evolution
over time (even beyond several different venture “attempts”).
We expect refined i* models to help increasing the foundational understand-
ing and static characterization of the two approaches whereas logic-based sim-
ulations are foreseen to shed some light on the dynamic characteristics. The
results of these investigations then need to be reviewed and applied to refine
and improve the quantitative agent-based simulations in Repast.
Altogether we can summarize that first steps toward a better understanding
of “effectuation” have been made. Yet more analysis and simulations need to
be run to better understand and answer the question when which of the two
approaches is highly-likely more valuable.
5 Ongoing and Future Work
Currently, we investigate, improve, and validate our i* process models for causa-
tion and effectuation, in particular in regard to which necessary feature can only
badly be represented in i*. At this point, we are still open in regard to whether
new features are needed in i* or – as it currently seems – some minor exten-
sions that have already been proposed by various authors over the years can be
combined to suit the new setting. Further on, we need to consider alternative
modeling approaches as well. As already for now, the agent-based simulations
are pursued rather independently from the i* modeling. Accordingly, other mod-
eling approaches such as systemic dynamics etc. similarly can provide valuable
input. Of relevance is only that any findings of these pilot modeling studies need
to be analyzed to complete, refine, and improve the models for the agent-based
simulations (in Repast).
To analyze simulation results and in particular due to the high importance
of networking, approaches from social network analysis as well as actor-network
theory are likely to become relevant in order to correctly evaluate and interpret
the results (see also [9]). This might also establish an interesting feedback loop on
(automated) adaptations of the i* models to reflect the outcome or evolution of
situations and settings throughout simulations. Further on, we need to calibrate
and validate the simulations by referring historic real world data before being
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able to derive guidance on the strengths and weaknesses of the two approaches
in regard to various different possible settings.
Acknowledgment. This research is funded in part by the RWTH Aachen Univer-
sity Excellence Project House “Interdisciplinary Management Practice” (IMP Boost
Project).
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