Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Applying Agent-Based Modeling to Integrate Bounded Rationality
in Organizational Management Research
Sven Meyer
European Business School (EBS)
[email protected]
Henrik Simon
European Business School (EBS)
[email protected]
Abstract
Simulation models to analyze the dynamics of
organizations have lately become more widely-used
in organizational science. In contrast to traditional
research, which is typically based on more stable and
predictable characteristics, these computational
models are better able to take into consideration the
dynamics and complex interrelationships of today’s
organizations.
Addressing the methodological question of how to
model organizations, we give an overview on
different computational approaches such as Discrete
Event Simulation, System Dynamics and Agent-Based
models to map these complex interactions between
organizations and their environments. Utilizing a
variety of criteria, we discuss the use and value of
different simulation approaches in organizational
theory and present both their advantages and
shortcomings.
In particular, while focusing on the information
processing view of organizations, we explain the
concept
of
bounded
rationality
and
its
implementation in simulation approaches. In this
context, we show that Agent-Based models represent
strong methodological tools to analyze both the
inherent organizational dynamics and the
coevolution between organizations and their
environments.
1. Introduction
The interplay between organizations and their
environments has been intensively studied during the
last three decades. Business organizations evolve as
their environments evolve around them while
political, sociological and economic factors drive
these changes in certain directions. Usually
organizations not only adapt to these changes but also
initiate them resulting in a dynamic coevolution
between the organizations and their environments.
This coevolution and especially the inherent
interdependences between organizations and their
Meike Tilebein
European Business School (EBS)
[email protected]
environments represent central determinants of
organizational behaviour.
Most of the research focusing on coevolution is
based on stable and predictable events either within
the organization or in the environment. This
assumption provides organizational researchers with
a broad foundation for theory development, but
disregards the complex dynamics of today’s
interconnected
and
continuously
changing
economies. The increased complexity of the business
world creates a significant challenge to
organizational research methods. In times of high
change empirical work is difficult to conduct [7].
Moreover, the high complexity of existing
organizational theories makes it difficult to develop
appropriate research designs. Often further
simplifications are needed in order to empirically
evaluate these theories [20]. In particular, the
modeling of the multifaceted interdependencies
between organizations and their environments by
intertwined variables impedes consistent results.
There is a need for alternative research methods in
organizational theory that explicitly consider the
emergent character of an organization and its
dynamic interplay with the environment.
In answer to this methodological challenge the
academic literature has proposed different
computational approaches to model the dynamic
interactions of coevolution. While empirically based
methods solely reflect the current or past state of an
organization, the computational models primarily
focus
on
the
dynamic
interactions
and
interdependencies either within organizations or
between
organizations
and
their
external
environments. They tackle the question: “What
happens if?” by simulating the processes of
organizational adaptation and decision making under
changing circumstances. As Gilbert [14] states,
simulation models of organizations represent a
legitimate way of analyzing the particular dynamics
of organizational societies. Therefore, they provide
researchers with an effective method to analyze
978-0-7695-3450-3/09 $25.00 © 2009 IEEE
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
organizational coevolution.
The theoretical foundation of most computational
simulation models in organizational theories builds
on the information processing view that goes back to
Simon [28]. His early studies of organizational
behaviour show the importance and potential of
analyzing
communication
streams
within
organizations. In this view organizational behaviour
is determined by the capacity of information
processing within the organization. When
environmental changes promote changes in the
organizational behaviour, organizations adapt to their
environment by solving a series of information
processing problems [23]. Since organizational
problem solving incorporates large quantities of
information, the task of information processing is too
complex to be carried out by a single subset of the
organization (e.g. a single member of the
organization). Accordingly, Simon suggests that
organizations are built to accomplish complex tasks.
Organizations provide individuals with a formal
structure for cooperation in order to achieve goals
that are beyond their individual cognitive capabilities.
In the academic literature these restrictions of the
individual’s information processing or cognitive
capability are known as bounded rationality [28].
The concept of bounded rationality contrasts with
the neoclassical understanding of organizations and
rational choice. Because the individual’s information
demand and processing capacity is limited under
bounded rationality, the individual’s decisions
seldom reflect rational choices. The decisions rather
depend on the information availability and
distribution within the organization which is set by
the underlying social network and formal
organizational structure [29].
Many theories of organizations do not consider
bounded rationality of individuals as a driver of
organizational behaviour. Theories establishing a
clear connection between the level of individual
behaviour and that of organizational behaviour are
missing. As Lomi and Larsen [20] state, there exists
no satisfactory theory of organizations that describes
the organizational behaviour rather than the
behaviour of selected organizational members. In
order to take bounded rationality into account, one
must describe the behaviour on both the individual
and the organizational levels at the same time.
Considering organizations as networks of
information processing nodes, the information
processing view can explain the interactions between
individual and organizational behaviour as well as
environmental change. Such networks enable the
analysis of how the distribution of information in the
organization and the information processing capacity
affects both the individual and organizational
behaviour: When environmental changes adjust the
information distribution within the organization, the
information processing view allows exploring the
responses of the individuals and the organization to
the changed circumstances. Conversely, change in
the organization’s information processing capability
can result in different responses to the current
environmental state. Consequently, networks of
adaptive information processing units can lead to a
sophisticated understanding of how organizational
structure, social networks and the external
environment impact both the individual and in sum
the organizational behaviour [1, 18, 24].
In this paper we illustrate that computational
modeling of organizations can be an appropriate
method to analyze the dynamic interplay between the
organizations and their environments, given bounded
rationality of the organizational actors. Here we focus
on the information distribution and processing
capability on both the individual and the
organizational levels. By considering organizations
as complex systems we review the state of simulation
in the field of organization theory and discuss their
shortcomings and advantages. In particular, we focus
on the ways of how bounded rationality is
implemented in the different simulation approaches
and discuss the impact to the simulation results. We
will show that Agent-Based Modeling of
organizations is a rich and promising approach of
modeling organizations, but highly depends on the
way of capturing bounded rationality. Finally, we
identify areas for further research.
This paper is organized as follows: The next
section presents organizations as complex systems
followed by an introduction to simulation approaches
in organizational research. Following that we
introduce and discuss the characteristics of different
simulation approaches like Discrete Event
simulation, System Dynamics and Agent-Based
Modeling. In reviewing these simulation methods we
evaluate their use in organizational research based on
a qualitative framework. Finally, the article
concludes with a discussion of the importance and
implementation of bounded rationality within
computational models of organizations along with
some directions for future work.
2. Organizations as Complex Systems
In order to introduce and compare different
computational research methods, we first lay down
our understanding of the research object
“organization” in a coevolutionary context. We refer
to the concept of coevolution by Baum and Singh [4]
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
and apply three theories; systems theory,
evolutionary theory and complexity theory.
Systems theory provides a framework to analyze
the complex and dynamic interactions between
systems and subsystems, respectively organizations
and their environments.
Evolutionary theory which is based on the two
major mechanism variation and selection that result
in adaptation is another important theory when
dealing with coevolution. While in classical
organizational theory selection and adaptation are
often seen separate, in a coevolutionary perspective
these mechanism are brought together as only those
organizations survive which are able to adapt well to
their environments. While evolution theory regards
organizations as unique entities with characteristic
behaviours, the combination of systems theory and
evolutionary theory additionally takes account of
knowledge, learning and decision-making on the
individuals’ level within organizations.
As change is driven by direct interactions as well
as feedback from the rest of the organizationenvironment systems [4], the analysis of
coevolutionary processes requires a multilevel
feedback approach. At this point one can refer to
complexity
theory
which
describes
the
“interconnection of all things in ever-expanding
layers of nested wholes” [34, p. 1385]. In this way
the organizational system is “a perceived whole
whose elements ’hand together’ because they
continually affect each other over time and operate
towards a common purpose.” [27, p. 90]
Thus, research methods used in this context
should be able to model the corresponding
fundamental traits of organizations and their
environments in a realistic and accurate way. In this
context, Bandte [2] derives 11 constitutive
characteristics of complex systems from literature
studies. In the following these characteristics are
briefly described and ranged into 4 sections:
Structure view, evolvement view, multilevel view
and existence view.
The structure view stands for the characteristics
that are given through the build-up and accoutrement
of complex systems (e.g. entities, connections and
differentiation from the corresponding environment):
Plurality and variety refers to the numerous
amounts and combinations of elements within a
complex system. Plurality stands for the number of
entities while variety describes their diversity [2]. As
the individuals’ behaviours shape the organizational
behaviour, plurality and variety plays an important
role in modeling coevolution.
Openness describes the ability of a system to
exchange material, energy and information with the
environment. Closed systems are bound to an internal
focus and any influence from the outside is disabled
by their design. In contrast, open systems either allow
the exchange with their surroundings or even
influence and adaptation [33]. Thus, we regard
organizations and their environments as open
systems.
Bounded rationality contrasts to the idealistic
assumption of complete knowledge distribution
among every system element which enforces rational
behaviour. Closer to reality is the confession of
irrational behaviour within complex systems, which
appears when elements are limited in their
information processing capacities or have no access
to complete information. The elements then act on
the basis of their given limited range of knowledge
and thus are supposed to form organizations in order
to carry out complex tasks [2].
The evolvement view focuses on the
characteristics incorporated with the interactions of
the system elements and the resulting effects on the
initial state:
Dynamics capture the behaviour of a system as
well as the change itself within an organizationenvironment system over time. Including time as a
dimension refers to the possibility of system
evolvement through intraorganizational activities
[27]. The dynamics of a system can be described as
periodic, at the “edge of chaos” or chaotic [15, 30].
Path dependence allows the analysis of past
behaviour. The system history reveals the
evolutionary paths which record how elements acted
and communicated to form the present system state.
As organizational behaviour is determined by past
events, exploring past data enhances understanding
and prediction of future system developments [25].
Feedback loops among the system entities are
another constitutive element of complex systems and
are as described an important concept for
coevolutionary studies. “For a system to be complex
it must be connected in such a way that multiple
causal loops are present that themselves interact with
each other” [25, p. 7].
Non-linearity takes into account that complex
systems may include activities which are neither
linear nor predictable and might result in varying or
proportional activity outcomes. This is especially
true, when organizational behaviour is set by the
behaviour of all individual organizational members.
In contrast, linear simulation approaches cannot trace
or simulate wide range decision effects of either
individuals or a group of entities [2].
The multilevel view refers to the characteristics
of the independencies and internal activities between
the micro and macro level in complex systems:
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Self-organization refers to the appearance of
system structure which is formed without any outside
influences. The form is solely derived from the
internal system elements. It reflects the need to build
organizations in order to accomplish complex tasks.
Evolvement in either time or space, maintenance of a
stable structure or the appearance of transient
phenomena might occur.
Emergence “[…] refers to the arising of novel and
coherent structures, patterns, and properties during
the process of self-organization in complex systems”
[15, p. 49]. This can be started by simple processes
on the micro level which lead to system adoptions on
the macro level. In organizations a result of
emergence are routines and informal organizational
structures.
The existence view captures the characteristics
which enable an own perception and regulation of
complex systems:
Autopoieses describes an inherent renewal
potential. Autonomous and complex systems are able
to use internal networks. They build and control own
elements and so form own boundaries to the
surrounding environment [22].
Self-reference is the capability of a system to
recognize its own system or organizational identity,
which is coined by the activities of its elements [21].
3. The Simulation Schools
In the field of organizational theory there exist
three main simulation principles up to now: Discrete
Event Simulations, System Dynamics and AgentBased Simulations [8, 16]. In the following section
we give a brief overview on how these methods can
be applied to model organizational research problems
and to which amount they succeed in modeling the
described constitutive complex system traits.
3.1. Discrete Event Simulation
Discrete Event Simulations model their
underlying scope of analysis by system states. A state
is defined by a single variable or a vector of variables
which represent the entities on the system micro
level. A value change in these variables causes a
whole system state change. This can only happen at
definite time points when an event is triggered.
Events can be considered as “an occurrence that
changes the state of the system” [3, p. 10]. So a
change within the system is initiated by events to
which the elements just react. They neither actively
behave nor communicate. In the time phase between
events the state variables remain unchanged. The
duration of these phases can vary as the events can
occur either in standardized or non-standardized time
intervals. To describe this procedure formally, a set
of consecutive events is used. If an event is triggered,
the following events take the open place in this set. In
consequence, the modeled organizational system
evolves over time with every triggering [7, 8, 16].
3.2. System Dynamics
System Dynamics goes back to the sociotechnical system movements in the fifties and is
heavily influenced by cybernetics and systems
theory. The approach was developed by Forrester
[12]. As a holistic approach to simulate complex
dynamic decision problems it offers the possibility to
analyze causal structures and their accompanied
behaviours. Hereby it takes the complexity, the
internal feedback loops and the non-linearity and of
social systems into account [16, 31].
Similar to Discrete Event Simulation, System
Dynamics uses variables to describe system states. A
specific characteristic is that these variables (here
called “stocks”) are not limited to entities or physical
items and that their type can be inconsistent [8].
Links (here called “flows”) to relate the state
variables with each other are realized via functional
equations. The flows are defined in terms of the first
derivates of the “stocks”. So the flows describe how
the rate of change in one variable impacts the rate of
change in another or even further linked variables. In
consequence, the simulation captures a dynamic
nature that otherwise could not be obtained [7, 8].
Essentially, a System Dynamic model is a set of
coupled equations, namely ordinary differential
equations and partial differential equations, which
represent changes to systems over time [14].
Additionally, non-linear flows, stochastic elements,
or events can be added for more complexity [7, 8].
The application of System Dynamics is in
particular useful for simulating a great number of
quantifiable and related variables. A transparent
display of relations on the macro level is given while
redundant influencing factors are eliminated. Overall
System Dynamics represents not a pure continuous
description of a trend but offers assumptions over the
dynamic co-operation of objects [2, 8].
3.3. Agent-Based Modeling
Agent-Based Modeling has its background in the
artificial intelligence area of modern computer
science. This simulation approach differs from
Discrete Event Simulations and System Dynamics by
its fundamental simulation technique, as systems are
not simulated through their states, but through their
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
internal autonomous elements (so called “agents”).
So the analysis takes place on the micro level instead
of the macro level of a modeled system. Agents are
designed to represent the individual objects of a
system (e.g. members of an organization). Franklin
and Graesser [13, p. 25] define an agent as “a system
situated within and a part of an environment that
senses that environment and acts on it, over time, in
pursuit of its own agenda and so as to effect what it
senses in the future.” So an agent is placed in some
environment and is able to autonomously act in this
environment in order to achieve its individual
objectives [35]. The definition links the construct
“agent” with certain classification profiles. These
profiles are important for the application in broader
simulation approaches and are described in the
following:
Autonomy: Agents are designed to have a certain
unique behaviour, which is determined by individual,
directly assigned goals. Idealistically, this implanted
order to fulfill their own needs lets them act
egoistically. So they do not necessarily account for
other agents at first sight [5, 13, 26].
Communication: Agents are not solely equipped
with individual goals that correspond with their duty
fulfilling behaviour. Additionally, they also have
embedded schemata which determine their
communication behaviour to other agents. If an agent
cannot reach his prescribed individual goals by
himself, he might be forced to communicate with his
environment, or more precise with other agents via a
formal language in order to find another acceptable
solution. So if it serves the attempt to reach his given
goals, the programmed schemata enables an agent to
teamwork or to bargain [36].
Reactivity and proactivity: Agents maneuver in a
special simulation environment in which the agents
Bounded Rationality
Openness
Plurality &
Variety
Table 1. The Structure View
Discrete Event Simulations
• The system elements can be
multifaceted modeled
• They face no limitations
• They fulfill the need for plurality
and variety
System Dynamics
• Only the state of one changing
object – the system - is described
• Within the system the modeling of
several parallel entities is possible
Agent-Based Modeling
• Agents can be equipped with different goals and
behaviours to represent intended system
heterogeneity
• Limited by the growing need for computer processing
power to handle the increasing amount
• Only allow the modeling of closed
systems
• Because of the lack of
communication abilities, influence
from the outside cannot be
considered
• Environmental impacts can be
included via state variables
• But this means they become a part
of the simulated structure and so of a
closed system
• New agents can be added successive
• The accoutrement with entities is not fixed
• Communication with the system’s environment can be
modeled
• All possible events are
deterministic or stochastic
• No modeling of limited information
distribution and non-rational
decisions (bounded rationality)
• Changes within a system are
determined by mathematical
unchangeable dependencies
• Bounded rationality cannot be
displayed
• Agents can be confronted with limited information
access and restricted information processing capacity
• Even under such simulated limitations they have to
make decisions
• This allows the implementation of bounded rationality
aspects
• “Agent-based Modeling is clearly a powerful tool in the
analysis of spatially distributed systems of
heterogeneous autonomous actors with bounded
information and computing capacity” [10, p. 56]
Non- Feedback
linear.
Loops
Path
dependence
Dynamics
Table 2. The Evolvement View
Discrete Event Simulations
• Realized via the event set method
• Not in a continuous form but in a
time discrete manner
System Dynamics
• Models system changes over time by
linking the state variables via
functional equations
Agent-Based Modeling
• Modeled systems are dynamic
• Agents steadily react to internal or outside impacts
• Dynamic effects can be controlled by the amount of
agents or by changing local rules
• Event set allows the analysis of
past behaviour
• System evolvement can be traced
• Identical initial states always lead to
the same result
• It does not matter which paths were
taken to reach the final state
• Therefore, the analysis of past
behaviour is not necessary
• Communication among the
system elements is not designed
• This disallows the establishment
of feedback loops
• Feedback loops on the macro level
are an important factor
• But feedback loops on an individual
level cannot be taken into account
• Agents can memorize simulation steps (impacts,
communications etc.) and adapt them on new
situations
• Predicting agent behaviour requires complete
acquaintance of the individual experiences
• These experiences determine the whole system
experience
• Communication is a part of the agent behaviour and
can be established directly from agent to agent or to
the environment
• Can be taken into account by
stochastic time phases between
triggered events
• Non-linear equations can easily
integrate non-linearity
• Rule-based behaviour implementation allows easily
installation of non-linear rules
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Emergence
SelfOrgaization
Table 3. The Multi-Level View
Discrete Event Simulations
• Event set steers and dominates
the actions of the system
elements
• Self-organization cannot be
modeled
System Dynamics
• The macro level perspective only
allows an aggregated view on the
individual behaviours and the
interactions of the elements
• Self-organization cannot be taken
into account
Agent-Based Modeling
• Agent systems are based on individual independent
entities
• Top-down control is not necessary
• New agents are influenced by the established
behavioural habits and experiences of the former
agents
• Focus lies on the micro level
• An aggregated impact on the
macro level structure cannot be
displayed
• The macro level perspective only
allows an aggregated view on the
individual behaviours and the
interactions of the elements
• Emergence cannot be taken into
account
• The interactions among agents on the micro level of a
system can lead into emergent structures up to the
macro level
• Local rules can so have an impact on the whole
system
• This can be strong enough to affect the whole system
behaviour
SelfReference
Autopoieses
Table 4. The Existence View
Discrete Event Simulations
• Focus lies on the micro level
• An aggregated impact on the
macro level structure cannot be
displayed
System Dynamics
• The macro level perspective only
allows an aggregated view on the
individual behaviours and the
interactions of the elements
• Autopoieses cannot be taken into
account
Agent-Based Modeling
• Systems can be designed to build and renew
themselves under the condition to reobtain the
system’s identity
• Event set steers and dominates
the actions of the system
elements
• Self-reference cannot be modeled
• The macro level perspective only
allows an aggregated view on the
individual behaviours and the
interactions of the elements
• Self-reference cannot be taken into
account
• Agents can be equipped with the ability of selfreflection
• And with the ability of self-judgment depending on the
implanted rules
can be designed to interact not only among
themselves, but also with their surrounding area. So
agents sense their environment and react adaptively.
If this is combined with a learning capability, their
situational behaviour cannot be determined and not
pre-estimated. So each agent follows an individual,
goal-oriented, and self-induced behaviour [13].
Goals and planning: Agents independently plan
and organize to achieve their purposes. They can
decide on which “acting” brings them further and
which self-defined sub-goals are worthwhile. They
do not tend to optimize their behaviour but try to
satisfy their incentives. So they are capable to accept
non-optimal solutions. “Agents adapt by moving,
imitating, replicating, or learning, but not by
calculating the most efficient action.” [17, p. 43]
Emotions: An interesting characteristic of AgentBased Modeling is the attempt to include realistic
aspects drawn closer to human behaviour. Agents can
be equipped with indicators that try to emulate
emotions like anger, joy, or fear. For instance, an
indicator could affect the agent’s communication
behaviour (e.g. lowered information content to
simulate anger). So behavioural regulations, which
obey to reliable psychological rules, are implied [2].
Heterogeneity: Each agent is designed to
simulate an individual entity. This avoids the need to
aggregate an average agent dummy to estimate data
about the single system elements. This narrows the
room for data cutting generalizations [2].
Anthropomorphism: Agent-Based models allow
to transfer human character traits into a digital model.
This can include for example the modeling of human
mindsets (e.g. goals, wishes, or attitudes) with
attributes like talkativeness or loyalty. The attempt to
install anthropomorphism offers a great research
potential for organizational theory [13].
Intelligence: The intelligence of an agent is
influenced by the quality and quantity of the
processed knowledge as well as the ability of using
this knowledge to draw conclusions. It comprises the
ability of using gained experiences fostering
continuous improvement and adaptation to the
environment [13].
Moreover, agents can also be distinguished into
three classes. The reactive agent receives information
from his environment or other agents, responds to
this and updates his inner state according to his
implied rules [9]. Intentional agents have the same
reaction potential of reactive agents but are
additionally endued with the ability to set and modify
sub-goals driven by motivational aspects (metarules). They can uncover conflicts, set priorities and
plan their way to reach their goals [9]. Social agents
additionally have the accoutrement to recognize the
strategies of other agents and take this knowledge
into account for their own planning. The first two
classes are already used by researchers, but the third
class is to this day still an ideal, which has not been
realized yet [2].
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
3.4. Summary
The adapted table 5 summarizes the
characteristics of the discussed simulation approaches
[2]. While it seems that Agent-Based Modeling is the
sole tool for organizational studies, we will
emphasize that both Discrete Event Simulation (e.g.
simulation questions tackling the structure view
section) and System Dynamics (e.g. simulation
questions tackling the evolvement view section) are
also important modeling tools in organizational
theory but are not sufficient to represent the complex
dynamics of coevolution. The results of our
evaluation which reflects the requirements for
modeling coevolution show the suitability of AgentBased-Simulation. However, Agent-Based Modeling
have several shortcomings in modeling coevolution
as we describe in section 4.1.
4. Bounded Rationality in Simulation
Models of Organizations
The discussed approaches of modeling
organizations based on the information processing
view differ in their capabilities to embody bounded
rationality. As Simon [28] characterizes, bounded
rationality describes to which extent the behaviour of
a person or an organization depart from pure
rationality in the sight of classic and neoclassic
theories. Often organizations show an irrational and
unexpected behaviour which contrasts to the classical
and neoclassical understanding of organizations as
intentional and rational systems. While the rational
choice paradigm of the classic theories explains most
economic problems like market and price
determinations well, it fails to describe the frequently
irrational behaviour of organizations. In particular,
rational models of organizations neither explain the
sharing of information processing tasks as a main
characteristic of organizations nor the existence and
functioning of administrative apparatus within
organizations [32]. If in respect to classical theories
information access and information processing
capabilities are unrestricted, there is no need to
collaborate within an organization: An agent alone
can manage a firm of arbitrary size since his
capability of information processing is unlimited.
The concept of bounded rationality can help to
overcome this shortfall and thus has become an
influential concept in organizational theory. A reason
for the importance mainly lies in the characteristics of
human decision [19]. In contrast to classical and
neoclassical theories, the concept of bounded
rationality assumes restrictions on the information
Table 5. Overview of the simulation abilities
Structure view
Plurality & Variety
Openness
Bounded rationality
Evolvement view
Dynamics
Path dependence
Feedback loops
Non-linearity
Multilevel view
Self-organization
Emergence
Existence view
Autopoieses
Self-reference
Discrete
Event
Simulation
System
Dynamics
AgentBased
Modeling
yes
no
no
no
no
no
yes
yes
yes
yes
yes
no
yes
yes
No
yes
yes
yes
yes
yes
yes
no
no
no
no
yes
yes
no
no
no
no
yes
yes
processing capacities. These restrictions affect the
decider’s ability to make rational decisions and foster
intraorganizational collaboration in order to achieve a
higher information processing capacity. Information
imbalances within organizations can amplify this
effect, even though they do not automatically lead to
irrational choices [19]. In particular, it is the
combination of information imbalances and restricted
information processing capacities that drives the
sharing of information processing tasks and so the
behaviour of organizations. Consequently, bounded
rationality can help researchers to understand
organizational decisions from a different perspective
than the one of classical and neoclassical theories.
In order to overcome bounded rationality,
decision making in organizations strongly depends on
information exchange and sharing of information
processing tasks [23]. Although bounded rationality
promotes collaboration between organizational
members, it simultaneously hinders the members to
make rational decisions in the sight of the entire
organization,
e.g.
maximizing
the
given
organizational goals. The resulting diversity in
individual decisions does not only shape
organizational behaviour but also makes it more
irrational and unexpected.
Therefore, bounded rationality becomes an
essential element in computational models of
organizations when organizational decision making
and interaction as well as organizational behaviour lie
in the main research focus. The impact of changes in
the external environment on organizations is such a
case. As external changes in the environment affect
dynamically the information distribution within an
organization, the patterns of collaboration and
decision making on both the individual and the
organizational level evolve correspondingly. In order
to model these inherent dynamics of the adaptation
process, simulation approaches are required that first
are able to represent bounded rationality, and second
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
can model the dynamic interplay between
organizations and their environment.
As the overview of different simulation
techniques in section 3 shows, only one of the
discussed simulation techniques allows first to
integrate bounded rationality into a simulation model,
and second to embody the complex relations between
external and internal organizational determinants.
While in Discrete Simulations and System Dynamics
bounded rationality of single actors cannot be
represented, Agent-Based models of organizations
offer techniques to implement bounded rationality
with a high degree of freedom. In Agent-Based
models information processing capacities can be
individually restricted as well as information
imbalances can be realized. Moreover, Agent-Based
models permit to represent complex, intertwined
relationships between organizational members as
well as between organizations and their
environments. Thus, they are the first choice when
studying organizational behaviour which is governed
by
organizational
decision
making
and
intraorganizational interaction
4.1. Bounded Rationality in Agent-Based
Models
In the literature on Agent-Based models of
organizations there are two major methodological
variants of modeling bounded rationality. Van Zandt
[32] calls them the constrained-optimal approach and
the non-optimal decision rule approach in respect to
their effects on organizational information
distribution and processing.
The constrained-optimal approach is generally
based on limiting either the information flow between
organizational agents or regulating the individual
information processing capacity. So it explicitly
affects the organizational behaviour by confining the
information processing capacities within the
organization. Regularly, it consists of two elements:
First, different constraints are introduced in the
decision procedure of an agent, e.g. restricting the
agent’s
maximum
amount
of
processable
information. For example, a possible decision rule
would be: “Only react to last three transactions” or
“Only react to information of your direct neighbours
(e.g. directly connected nodes in the information
processing network).” Second, under given
circumstances the modeler identifies the optimal
decisions, characterizes them and evaluates them
with different probabilities.
The constrained-optimal approach is especially
useful for analyzing organizational designs, because
restrictions on the individual information processing
capacities do not directly influence organizational
design settings, the connections between the nodes in
the information processing network.
The second variant, the non-optimal decision rule
approach, does not limit the information processing
capacities either of an individual agent or of the
organization. Rather it incorporates non-optimal
decision rules into the simulation model. In contrast
to the constrained-optimal approach the non-optimal
decision rules are independent from changing
circumstances.
In particular, the non-optimal decision rule
approach is useful when either decision rules should
not be immediately influenced by changing
organizational circumstances, or when restrictions in
the organizational information processing are
difficult to realize. Especially in Agent-Based models
of organizational learning the non-optimal decision
rule approach has been widely used.
While both approaches are independent solutions
to model bounded rationality in Agent-Based models,
both methods are often combined in order to extend
the existing models by so far unmodeled bounds of
rationality [32]. A combination of both methods
allows to model bounded rationality on different
functional and organizational levels. On the one
hand,
the
constrained-optimal-approach
can
continuously influence the information processing
capability of the organization. For example, this
approach would be appropriate for characterizing
evolutionary organizational processes. On the other
hand, the non-optimal decision rule approach
incorporates further decision rules which hardly adapt
to changing circumstances. It enables modelers to
incorporate further mechanisms or trends that directly
modify the computed decision rules of the
constrained-optimal approach.
When considering the impact of the external
environment to an organization, the combination of
the two described approaches would be well suited.
Stable environmental trends can be modeled by nonoptimal decision rules, whereas the dynamics of
organizational adaptation are realized by the
constrained-optimal approach.
The combination of the two approaches offers
several advantages. On the one hand non-optimal
decision rules make it possible to simulate long-term
trends. This might be especially useful when
considering that the change in organizations due to
environmental impacts does not occur immediately
but takes place on a longer time-scale. For example,
in modern organizations the adaptation of decision
procedures to changed environmental circumstances
evolve over several years in a slow, but continuous
process of incremental learning, imitation and
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
adaptation [32]. On the other hand, constraints in the
information processing capacities (constrainedoptimal approach) induce the evolution of
organizational designs by altering the sharing of the
information processing tasks in the organization.
Consequently, in order to model the dynamic
interplay between organizations and their external
environments, bounded rationality is an important
concept to cover these diverse adaptation processes.
Without considering bounded rationality, AgentBased models would fail to explain organizational
design and behaviour in correspondence to the
external environment. However, since different
methods exist to incorporate bounded rationality in
Agent-Based models, bounded rationality does not
rule out any specific behaviour [6]. If the simulation
setting is kept constant, a different approach of
incorporating bounded rationality in the model would
provide changes in the simulation results. Thus,
depending on the implementation of bounded
rationality, a specific Agent-Based model can provide
arbitrary and indeterminate results.
Due to this variance in the results some
researchers conclude that Agent-Based models of
organizations with bounded rationality yield very
little in terms of theory development [11]. Because
theory development requires consistent and replicable
simulation results, the different possibilities to model
bounded rationality represent a significant weakness
in Agent-Based models of organizations. Therefore,
further concepts are required in order to better
understand the effects resulting from the different
ways of employing bounded rationality in
organizational simulation models.
In this context, the validation of Agent-Based
models of organizations plays an important role.
Agent-Based models of organizations, especially
when including bounded rationality, require a
thorough validation to what extent the simulation
model reflects the organizational reality. Often
organizational research that uses simulation
techniques disregards this point [6]. When designing
a research model, the challenge is to narrow the
research problem down to a specific context instead
of studying universal relationships [7]. In the latter
case simulation will yield inconsistent and complex
results, whereas in the first case simulation models
which tackle a distinct, narrowed problem can
establish a tight link between the real and simulation
world. Such a link does not only support validation of
the Agent-Based model but also yields a broad
foundation for theory development. Therefore,
Agent-Based models that address a distinct problem
can be a strong analytical tool for analyzing the
coevolution of organizations and their environments.
5. Conclusion
In this paper we have given an overview about
simulation techniques in organizational theory based
on the information processing view and demonstrated
that simulation can be considered as a promising
additional approach of research. Simultaneously, we
have discussed the need for a tighter link between the
real and simulation world as well as have emphasized
the importance of validating simulation models.
In contrasting the different simulation approaches
by a qualitative framework we have discussed their
benefits and shortcomings. Especially, we have
focused on how computational models can represent
the coevolution between organizations and their
environments. and highlighted the concept of
bounded rationality. Finally, we have concluded that
Agent-Based models not only provide a broad field of
applications in organizational research, but also are
applicable to model the dynamics of coevolution.
The strength of Agent-Based models in
organizational theories is their integrative character.
Agent-Based models permit to unify different
academic disciplines like social sciences, economics
and natural sciences. The models, however, are not
appropriate to explain universal relations between
organizations and their environments; rather they are
strong analytical tools to study complex dynamics of
distinct problems out of the real world.
The applied information processing view of
organizations combined with the concept of bounded
rationality has the distinctive advantage to explain
specific characteristics of organizations like the
interactions between organizational members,
emerging organizational designs or the coevolution of
organizations and their external environments.
However, one should keep in mind that the concept
of bounded rationality has a very general character
compared to the strong rational choice paradigm of
the classical and neoclassical theories. Event though
the idea behind bounded rationality has proven to be
realistic, the concept is too weak to determine a
distinct behaviour. This shortcoming is reflected in
the broad variety of modeling bounded rationality in
organizational contexts. In particular, the different
implementation techniques of bounded rationality in
Agent-Based models can blur distinct simulation
results. In frequent cases the choice by which
technique bounded rationality is implemented seems
arbitrary. Maybe this explains why neoclassical
researchers still insist on the rational choice
paradigm. Thus, further research is needed of what
distinguishes different techniques to model bounded
rationality and of how modeling techniques impact
the simulation results. Answers to these research
9
Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
questions could improve not only the strength of
simulation methods in organizational theories but
also lead to a better understanding of how
organizations evolve in highly connected and
dynamic environments.
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