J PROD INNOV MANAG 2005;22:380–398
r 2005 Product Development & Management Association
Uses of Agent-Based Modeling in Innovation/New Product
Development Research
Rosanna Garcia
Little has been written in the new product development literature about the simulation technique agent-based modeling, which is a by-product of recent explorations
into complex adaptive systems in other disciplines. Agent-based models (ABM) are
commonly used in other social sciences to represent individual actors (or groups) in
a dynamic adaptive system. The social system may be a marketplace, an organization, or any type of system that acts as a collective of individuals. Agents represent autonomous decision-making entities that interact with each other and/or
with their environment based on a set of rules. These rules dictate the behavioral
choices of the agents. In these simulation models, heterogeneous agents interact with
each other in a repetitive process. It is from the interactions between agents that
aggregate macroscale behaviors or trends emerge. The simulated environment can
be thought of as a ‘‘virtual’’ society in which actions taken by one agent may have an
effect on the resulting actions of another agent.
This article is an introduction to the ABM methodology and its possible uses for
innovation and new product development researchers. It explores the benefits and
issues with modeling dynamic systems using this methodology. Benefits of ABMs
found in sociology and management studies have found that as the heterogeneity of
individuals increase in a system or as network effects become more important in
a system, the effectiveness of ABMs as a methodology increases. Additionally, the
more adaptive a system or the more the system evolves over time, the greater
the opportunity to learn more about the adaptive system using ABMs. Limitations
to using this methodology include some knowledge of computer-programming
techniques.
Three potential areas of research are introduced: diffusion of innovations, organizational strategy, and knowledge and information flows. A common use of
ABMs in the extant literature has been the modeling of the diffusion process between networked heterogeneous agents. ABMs easily allow the modeling of different types of networks and the impact of these networks on the diffusion process. A
demonstrative example of an agent-based model to address the research question of
how should manufacturers allocate resources to research (exploration) and development (exploitation) projects is provided. Future courses of study using ABMs
also are explored.
Address correspondence to: Rosanna Garcia, Northeastern University, College of Business Administration, 202 Hayden Hall, Boston, MA
02115. Tel: (617) 373-7258; E-mail: [email protected].
I wish to sincerely thank Paul Rummel for his coding expertise in building this model and Amit Joshi for his research assistance in testing proofof-concept for several different ABMs. Comments were received from the Product Development & Management Association International Conference 2002 and the members of the Institute for Global Innovation Management at Northeastern University. In particular, I would like to thank
former editor Abbie Griffin for her encouragement and feedback in helping to bring this article to fruition. Additionally, two anonymous reviewers
provided feedback, which was greatly influential in developing this article.
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
D
evelopments in the nonlinear sciences have
transformed the subject of organizational
complexity. A new perspective has emanated from the Santa Fe Institute where organizations
are treated as adaptive systems (Holland, 1975) that
must match the complexity of their environment in
order to optimize performance (Boisot and Child,
1999). Complex adaptive systems (CAS) have been
used extensively in modeling physical, chemical, and
biological systems and have only recently been accepted within the management sciences. ‘‘CAS models
represent a genuinely new way of simplifying the complex, of encoding natural systems into formal systems.
Instead of making nonlinear systems tractable by reducing them to a set of causal variables and an error
term, CAS models typically show how complex outcomes flow from simple schemata and depend on the
way in which agents are interconnected . . . CAS models afford exciting new opportunities for analyzing
complex systems without abstracting away their interdependencies and nonlinear interactions’’ (Anderson, 1999, p. 220).
The role of complex adaptive systems has largely
been ignored in the innovations literature. Although
there is a long tradition of using simulation in innovations research (Frenzen and Nakamoto, 1993;
Garcia, Calantone, and Levine, 2003; Granovetter
and Song, 1986; Levinthal and March, 1981; Nelson
and Winter, 1982; Repenning, 2001), these approaches do not consider evolving systems that can adapt
to the dynamic internal and external environments
influencing the innovation process. More importantly,
methodological approaches to studying CAS have
not been considered in these literature streams. This
article acts as an introduction to a methodology
for studying complex adaptive systems commonly
referred to as agent-based modeling (ABM). Possible
uses of ABMs in innovations/new product development research, along with a demonstration on how
to build an ABM, are introduced in the following
sections.
BIOGRAPHICAL SKETCH
Dr. Rosanna Garcia is assistant professor of marketing at Northeastern University in Boston. She received her Ph.D. in marketing
from Michigan State University with an emphasis in new products.
She also worked for a number of years in industry as an entrepreneur and new product manager. Her research interests are in
innovations, complex adaptive systems, and the diffusion of discontinuous innovations. She has published articles in the Journal of
Product Innovation Management and Decision Sciences.
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381
Agent-based modeling differs from other simulation methods, as the primary unit of study is the
agent, or individual. The agents represent heterogeneous entities that interact with each other or with
their environment. In an agent-based model, the programmer only models the behaviors of an individual.
These individual agents then interact with each other
in a repetitive process. It is from these repeat interactions that global or macrotrends and behaviors
evolve. Although the agents often appear to act together as a group, they are in fact just a collective of
individuals influencing each other’s microdecisions. It
is from the interactions between agents that aggregate
macroscale behaviors emerge. Agent-based models
have been used to model insect behavior, urban systems, traffic-flow dynamics, and even civil violence
(Rauch, 2002), which are all excellent examples of
how individual behavior can result in a global group
behavior. Thus, ABMs are particularly useful in simulating the dynamic interactions (direct or indirect)
between entities (e.g., queen bees and worker bees,
autos and roadways, consumers and innovations) coexisting in a cohesive environment. The simulated environment can be thought of as a virtual society in
which actions taken by one agent may have an effect
on the resulting actions of another agent.
Another major difference between agent-based
modeling and other simulation techniques is the
former’s focus on the adaptiveness of the agents within the system. In ABMs, agents are usually modeled as
heterogeneous individuals so that each and every
agent in the model can have unique characteristics
and rules (decision-making patterns) it follows. This
heterogeneity of agents allows more realistic representation of real-world phenomena compared to
models that assume homogeneity or assume that
individual agents follow an average behavior. Because
each agent is seen as an individual, it can adapt at
its own pace and in its own unique way as influenced
by macrolevel system changes. ABMs, which follow
genetic algorithms (cf. Bruderer and Singh, 1996),
provide a demonstrative example of adaptive agents.
Genetic algorithms model the agents as entities that
follow a Darwinian framework of evolution; thus, the
DNA of an organization or individual consumer can
change over time as its environment changes, yet each
agent changes at its own pace. These types of dynamic
microlevel changes are difficult to model in other simulation techniques.
Another important difference between ABM models and other simulation techniques is its ease of use
382
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even for noncomputer programmers. There is no need
to understand differential equations, integrals, or even
statistics. Because the primary focus is on the individual agent, a modeler must only define the behaviors,
rules, or policies that a particular type of agent follows. These rules determine how agents interact within
the system modeled. Knowledge to set the rules can
be gained from qualitative or quantitative research
as well as through theory. The behavior rules of the
agents are the custom building blocks that come together to simulate a system (Holland, 1995). Additional rules or laws that govern either the entire system
or individual agents can be added piecemeal, thereby
slowly increasing the complexity of the system.
It must be noted that CAS models and ordinary
causal models are complements, not rivals. It is not
necessary for scholars to adopt one or the other as
the best way to analyze organizations. CAS models
build on the foundation of causal theories, explaining
observed regularities or, even more importantly,
helping to explain irregularities of a model. The objective of ABMs is to build theory rather than to construct a descriptively accurate or predictive model
of organizations in contingent environments. Good
CAS models should not only help to understand
established findings but also should assist in identifying causal relationships that have previously gone unexplained. A complex systems approach is congruent
with business enterprises today. Dell is an example of
one firm that uses the holistic model of business to
shorten the feedback loop between customer needs
and design–production–inventory processes. Although its primary focus is on the customer, Dell is
mindful of its entire environment and develops strategies to address the total evolving marketplace.
This article focuses on agent-based modeling as a
methodology for studying complex adaptive systems
as related to the innovation process. The benefits and
usefulness of ABMs are introduced first, followed by
possible applications for using them in studies regarding new product development (NPD) and innovation
issues. A simple model also is provided and summarized to demonstrate ABMs in a competitive research
and development (R&D) environment. The article
concludes with issues and future research focused on
NPD and innovation using ABMs.
Benefits of Agent-Based Modeling
The benefits of ABM over other modeling techniques
are numerous. Shortcomings, of course, also exist and
R. GARCIA
will be discussed in the final section. However, the
benefits of this methodology outweigh the shortcomings if the ABM mindset is understood. The role of
simulation is not to create an exact facsimile of any
particular system or environment but to assist in the
exploration of the consequences of various contingencies. In other words, simulation should be used as a
tool for the refinement of theory (Bonabeau, 2002).
By simulating an approximation of real-world behavior that may be difficult to capture in static models, the ABM approach focuses on how processes
evolve over time and how policies might be changed
to affect the outcomes of an evolving system. This
methodology accords with Axelrod’s (1997) description of the value of simulation: ‘‘Simulation is a third
way of doing science. Like deduction, it starts with a
set of explicit assumptions. But unlike deduction, it
does not prove theorems. Instead a simulation generates data that can be analyzed inductively. Unlike
typical induction, however, the simulated data comes
from a rigorously specified set of rules rather than direct measurement of the real world. While induction
can be used to find patterns in data, and deduction
can be used to find consequences of assumptions,
simulation modeling can be used to aid intuition’’
(pp. 24–25). If ABMs are accepted as a learning tool
to guide intuition in refining innovation theories,
they can be useful to the new product development
researcher.
ABMs are best suited to domains where the natural
unit of analysis is the individual (e.g., consumer, firm,
employee) and when both microlevel behavior of individuals and macrolevel patterns from the interactions of these individuals are of interest. Modeling
ABMs require understanding the behaviors of the
agents and translating these behaviors into rules for
agents to act upon in the modeled environment. Social
researchers frequently observe behavioral patterns
that cannot be easily described analytically but can
be depicted through ‘‘what-if ’’ scenarios. ABMs provide a methodology in which these patterns can be
replicated and then manipulated to study contingent
outcomes. One can easily study individual agents (microlevel), subgroups of agents, and aggregated agents
(macrolevel) behaviors with different levels of rules
coexisting in a single model. Due to this disaggregation, dynamic environments can be created where
agents enter and exit the system (i.e., underperforming
firms leave the marketspace—the marketplace in a
simulated environment—or new competitors enter
the marketspace). The phenomena of interest are the
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
impact at the macrolevel from changes occurring at
the microlevel.
As previously noted, a major component of ABMs
is the dynamic nature of the agent characteristics and,
hence, the system. System dynamics can result in
emergent phenomena (Holland, 1998; Vriend, 1995)
or coevolving systems. Various definitions have been
given for emergence; emergent behaviors are defined
here as behaviors not explicitly modeled by the programmer but instead resulting from the simple rules
that dictate the interactions of agents. It has been
said that in emergent systems, the whole is more than
the sum of parts (Holland, 1998). This refers to
the simple concept that unpredictable macrolevel
events can occur based on processes evolving at the
microlevel. Tesfatsion (2002) provides an excellent
summary of emergence in the related field of economics, also called agent-based computational economics.
She refers to emergence in an evolving system of
interacting agents as growing economies from the
bottom up.
Coevolution also can accompany emergence. In
many ABMs, agents may change their characteristics
or choice patterns over time. Agents make decisions
that alter their states, but these same agents influence
their neighboring agents to alter their own states as
well. When agents directly influence other agents’
choice decisions, this is said to result in a coevolving
system.
ABMs are useful when individual behavior is nonlinear such as when learning or adaptation occurs on a
microscopic level. For example, models where individual behavior exhibits memory, path-dependence,
hysteresis, non-Markovian behavior, or temporal correlations are easily modeled with ABMs. Stochasticity
also can be applied to the agents’ behavior. With
ABMs, sources of randomness can be applied at the
appropriate decision-making level as opposed to entering noise terms more or less arbitrarily, which is
typical in modeling stochastic processes.
ABMs also make it easier to distinguish physical
space from temporal space, which is useful in modeling social networks of diffusion and global location
of innovation project team members (both further
discussed in this article.) The study of the spatial
make-up of social networks has been a major focus of
study in recent years (Barabási, 2002; Valente, 1995;
Watts and Stogatz, 1998). Watts and Stogatz (1998)
argue that local interactions between network members have global consequences but that the relationships between local and global dynamics depend on
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383
the network’s structure. Garcia, Zhao, and Calantone
(2003) demonstrate how different types of social networks can affect the rate of diffusion. They tested four
different network structures for effects on the diffusion process of technologically advancing products: a
random network (Erdo+ s and Rényi, 1959), a cellular
automata network (Goldenberg, Libai, and Muller,
2002), a small-world network (Watts and Stogatz,
1998), and a scale-free network (Albert and Barabási,
2002). They found that when high uncertainty of performance accompanies the innovation, small-world
networks are more beneficial in speeding the diffusion
of the innovation compared to scale-free networks.
However, it should be noted that many ABMs are not
spatially dependent.
One of the reasons underlying ABMs’ popularity in
other social sciences is its ease of implementation.
ABMs are easier to construct than other analytic
models, although some computer programming knowledge is useful. It is reasonably easy to create an ABM
using Microsoft Excel or Mathworks Matlab. Additionally, several ABM-specific open-system software
programs are available for download (see Appendix
A). More sophisticated ABMs sometimes incorporate
neural networks, evolutionary algorithms, genetic
algorithms, and other learning techniques to allow
realistic learning and adaptation and therefore are
computationally more complex (Bonabeau, 2002).
To summarize, ABMs are useful
when both macro- and microlevels of analyses are
of interest (e.g., adoption [micro] and diffusion of
innovations [macro])
when social systems can easily be described by
‘‘what-if ’’ scenarios but not by differential equations (e.g., organizational cultures)
when emergent phenomena may be observed
(e.g., emergence of innovations)
when coevolving systems interact in the same
environment (e.g., competitive markets)
when learning or adaptation occurs within the
system (e.g., R&D collaboration)
when physical space and temporal space are of
interest (e.g., supply chain networks)
when the population is heterogeneous or the
topology of the interactions is heterogeneous and
complex (e.g., social networks).
Possible applications for ABMs in the study of
NPD and innovations are still evolving. See Table 1
for potential research issues ABMs could address.
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Table 1. Possible Research Issues for ABMs
Diffusions of
Innovations
Effects of network externalities
Word-of-mouth networks
Modeling tipping points
Social networks and viral marketing
Organizations
Innovation networks and
collaboration
Coevolution of competitive strategies
R&D emergence of innovations
Portfolio management
Innovation strategies and external
environmental influences
Knowledge/
Information Flows
Supply chain networks
Innovation/R&D collaboration
(inter- and intraorganizational)
Technology transfer (inter- and
intraorganizational; to and from
customers–lead users)
Strategy planning (organizational)
These issues are discussed in further detail in the next
sections.
Diffusion of Innovations
Modeling the diffusion of innovations in social networks has been the most common application for
ABMs to date (e.g., Bonabeau, 2002; Guardiola et al.,
2002; Nyblom et al., 2003; Valente, 1995; Young,
1999). Those researchers investigating the use of
ABMs in the diffusion of innovations found that network structure and spatial proximity of social networks significantly impacted the diffusion rate.
Additionally, they found that ‘‘agent-based modeling
is increasingly necessary as the degree of inhomogeneity increases in the modeled system’’ (Bonabeau,
2002, p. 7286).
It is straightforward to translate the prototypical
diffusion model originating from Bass (1969) into dynamic simulations of heterogeneous consumers using
ABMs. Consumers are modeled as agents who make
adoption or purchase decisions based on word-ofmouth influences from local interactions with other
agents (consumers). Other agents can be modeled as
influential consumers, seeding agents (hired guns to
positively influence potential adopters), firms with
marketing campaigns, or other types of mass media.
This is analogous to the Bass (1969) model where
there are innovators and imitators, each adopting an
innovation at different times. Unlike the seminal Bass
model, ABMs allow the introduction of heterogeneity
with respect to initial perceptions, adoption thresh-
R. GARCIA
olds, and even individual responsiveness to information so that more realistic marketspace environments
can be simulated.
Dawkins (1976) introduced the idea of memes
(rhymes with ‘‘seems’’) as contagious information
patterns that infect humans’ minds to alter their
behavior, causing them to propagate the patterns
among a population. Typical patterns of memes are
slogans, catch phrases, melodies, icons, inventions,
and fashion trends. Seeds, or carefully planted influencers, can initiate the propagation of the memes. The
connection between memes and effective advertising
campaigns is evident. Remember the ‘‘Where’s the
Beef?’’ campaign? Gladwell (2000) examined the role
of memes in diffusion as the search for ‘‘tipping
points,’’ and Rosen (2002) examined them as ‘‘buzz’’
or word-of-mouth influence. Although researchers
utilizing ABMs have not incorporated the use of memes in modeling diffusion, using this simulation technique provides great opportunity for understanding
the impact of such phenomena on diffusion.
Recent ABM studies have shown that diffusion
patterns are affected by different types of social networks (Garcia, Zhao, and Calantone, 2003; Janssen
and Jager, 2002; Valente, 1995). Scale-free networks
(Albert and Barabási, 2002) have been found more
effective in disseminating information and knowledge
compared to either small-world networks (Watts and
Strogatz, 1998) or random networks (Erdo+ s and
Rényi, 1959). Scale-free networks are characterized
by an uneven distribution of connectedness between
nodes or agents. Instead of a random pattern of connections, some agents are ‘‘very connected’’ and act as
hubs (for example, the internet). These hubs dramatically influence the way the network operates. These
past ABM diffusion studies, however, have not considered the effects on diffusion by the strength of the
ties (Granovetter, 1973) or by the relationships that
bind the ties. The studies of networks on diffusion
have primarily emanated from sociology studies and
not from innovation/new product development studies, so again there is great opportunity here for future
advancements.
Organizations
Modeling organizational structures and innovation
strategies have been other prominent applications
for ABMs (Dawid, Reimann, and Bullnheimer,
2001; Debenham and Wilkinson, 2003; Gilbert,
Pyka, and Ahrweiler, 2001). These researchers found
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
that ‘‘because innovation, imitation, and process improvements are not deterministic processes . . . This
makes the whole system a highly non-linear one’’
(Debenham and Wilkinson, 2003, p. 45). Thus,
agent-based modeling simulations are good methodologies in which to examine different strategies in the
innovation process. Agents in these models represent,
for example, adaptive firms, research labs, or markets,
each generating knowledge bases that lead to innovation. The dissemination of knowledge or information
between agents can easily be modeled to represent
collaborative or competitive strategies.
Gilbert, Pyka, and Ahrweiler (2001) considered innovation networks as evolving from the dynamic and
contingent linkage of heterogeneous units, each possessing different bundles of knowledge and skill. In
order to study these coevolutionary types of systems,
the Self-Organizing Innovation Network (SEIN) project (SEIN, 2000) was established by the European
Commission Framework 4 Programme. Several studies focusing on international innovation networks
have come from this now defunct project. The conclusions of this group were that ‘‘in short, [innovation]
networks are complex adaptive systems: they are generally self-organising, adaptive to their environment,
have no central control mechanisms, and their current
state is dependent on their past history. Innovations
can be seen as emergent and unpredictable outcomes
of the operation of networks. These characteristics
make it hard to apply the usual forms of analysis’’
(SEIN, 2000, p. 3). Thus, it was concluded that multiagent simulation models were best utilized to study
these types of networks.
Another organizational trend, the increasing orientation toward internationalization with regard to
innovation processes, leads to issues not only of structure but also of time and space. Internationalization
of product development teams results in globally located team members working on the same product
from different localities. This global location requires
coordination across time zones and geographical regions, as well as across cultures. ABMs can be used to
answer how cooperation and communication between
groups might be established to optimize design times
and time to launch. Since ABMs can be modeled as
heterogeneous individuals, even cultural issues can be
modeled into these studies. Collaboration and the
strength of weak ties in linking innovating partners
can easily be modeled using agents each possessing
their own decision-making criteria. This is a research
area currently uncharted.
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385
Scale-free networks also occur in organizations
(Powell, 1998; Powell, Koput, and Smith-Doerr,
1996). Powell, Koput, and Smith-Doerr (1996) studied the formation of alliance networks in the U.S.
biotechnology industry and discovered several key
players or hubs: companies such as Genzyme, Chiron,
and Genetech have a disproportionately large number
of partnerships with other firms. Although these authors did not utilize ABMs in their research, studying
collaboration and information dissemination hubs
can be accomplished through ABMs. Interorganization networks, as well as intraorganizational ones, can
be modeled as ABMs. Issues that can be addressed are
co-location of departments, R&D–marketing integration, information dissemination between different
departments, and innovation networks structures
(hierarchical versus loosely structured).
It is also possible to develop simulation models to
evaluate the effectiveness of different types of innovation strategies in different types of environments.
Debenham and Wilkinson (2003) modeled firm survival with different strategies in competitive environments, and Garcia, Rummel, and Calantone (2004)
modeled a learning strategy for solving the innovator’s dilemma (Christensen, 1997). In organizational
studies, the interest lies in how organizational structure and strategy should evolve as the environment
(e.g., competitors, demand, regulation, technology)
is evolving. Again, ABMs provide a unique learning
environment to evaluate these different conditions.
Knowledge/Information Flows
Innovation has long been recognized as benefiting
from the collaboration of manufacturers with their
customers, supply chain network, and other industry
partners (von Hippel, 1988). Tapping external knowledge sources is an important product development
strategy. In this respect, innovation networks have
been considered as an efficient means of studying
complex R&D processes (Gilbert, Pyka, and Ahrweiler, 2001). Researchers have recently begun to see the
advantages of modeling the transfer of knowledge
using agent-based modeling (Gilbert, Pyka, and
Ahrweiler, 2001; Iyer, 2002). Increasing complexity
of innovation networks, the accelerating rate of technology transfer, and shortening of product life cycles
have been considered responsible for the rising importance of innovation networks (Malerba, 1992).
Studying these types of complex systems is ideally
suited for ABMs.
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ABMs also have been used for modeling supply
chain networks. Parunak, Savit, and Riolo (1998)
used the DASCh Project (dynamical analysis of supply chain) to explore the dynamical behavior of a
manufacturing supply network. From an industry
perspective, Procter & Gamble (P&G) is reportedly
using agent-based modeling to help it improve its
supply chains. In fact, the Cincinnati-based maker of
Tide, Crest, Pepto Bismol, Clairol, and 300 other
household products now models its distribution channels to 5 billion consumers in 140 countries as a ‘‘supply network’’ (Anthes, 2003). The simulations let
P&G perform ‘‘what-if’’ analyses to test the impact
of new logistics rules on key metrics such as inventory
levels, transportation costs, and in-store stock-outs.
They have investigated alternate rules on ordering
and shipping frequencies, distribution center product
allocation, and demand forecasting. Key research issues regarding supply networks or webs (as opposed
to chains) and ABMs include logistic network configurations, emergence of coordination and integration
in supply webs, and evolution of supply network
relationships through market and price signals
(Lairson, 2003), all issues of importance when
launching new products.
In summary, the extant literature has just barely
touched upon the usefulness of agent-based models
in innovation research. Most published studies originate from the sociological and computer sciences
literature on innovations and often are theoretically
based with no supporting test of the theory.
Those studies with accompanying ABMs simulations
have found that as the heterogeneity between agents
in the study of interest increases, so does the
effectiveness of agent-based models. There has also
been a direct connection between the advantages of
agent-based models and modeling network effects.
The greater the connectivity between agents, the
greater the need exists for a methodology such as
ABMs in order to model the variety in degree of
connectedness and strength of ties. This is true
for modeling social consumer networks as well as
innovation networks of organizations. Likewise,
the more the agents or their environments evolve
over time, the greater the opportunity to learn
more about the adaptive system using ABMs. By
no means are the suggestions presented here meant
to be all-inclusive but instead serve as starting points
for fruitful areas of exploration. The next section
presents guidelines on how to build an agent-based
model.
R. GARCIA
An Example
It should be stressed that this example is for illustrative purposes only and is not to report any specific
findings. Garcia, Rummel, and Calantone (2004) provide the theoretical foundation for this example and
further detail of the results. Due to space restrictions,
only a summary of the functionality of the model is
provided.1
This example examines the dilemma of allocating
the right proportion of resources to exploration for
new technologies versus exploitation of existing
knowledge of an innovating firm. ABMs are uniquely suited for examining this issue. In fact, a couple of
other agent-based models (Dawid, Reimann, and
Bullnheimer, 2001; Debenham and Wilkinson, 2003)
have looked at the arduous problem of how resources
should be allocated to different types of R&D projects. These models are highly technical, complex, and
not based on case studies or other empirical data.
More advanced readers may want to refer to these
other studies for an alternative perspective. A simplified version of the exploration–exploitation issue is
presented here as a learning tool to better appreciate
the fundamentals of ABMs.
A first step in designing ABMs is to define the research question. Exploration and exploitation activities compete for scarce resources, and organizations
often must make explicit decision-making policies for
allocating resources between the two different types of
projects. Levinthal and March (1981) warn of the
danger of exclusively engaging in either exploration or
exploitation. Organizations that engage in exploration to the exclusion of exploitation are likely to find
that they suffer the costs of experimentation without
gaining many of its benefits. Conversely, systems that
engage in exploitation to the exclusion of exploration
are likely to find themselves trapped in suboptimal
equilibria. Maintaining an appropriate balance between the two different types of projects is a major
dilemma for many innovative firms. Thus, the research question posed is, ‘‘How should a firm allocate
limited resources to different types of innovation
projects in dynamic competitive marketplace in order
to maximize performance?’’
1
This example, modeled as a Netlogo ABM, is available at http://
igimresearch.cba.neu.edu/netlogo/jpim (Garcia and Rummel, 2004).
Visit this website to learn details about the mechanics of this ABM.
Also take the opportunity to run the model through various scenarios
to begin to understand its underpinnings. This model is best viewed by
downloading the Netlogo freeware (Wilensky, 1999); however, it is also
viewable without the download.
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
After identifying the research question, six major
model development components are detailed: (1) theory operationalization through a cognitive map
creation; (2) agent specification; (3) environmental
specification; (4) rules establishment; (5) measurement/data recording; and (6) run-time specification.
Following these six steps not only will establish the
computer program flowchart but also will determine
the information necessary to instantiate and support
the model. These six steps need not be followed in sequential order, for as the model is developed, it will be
necessary to return to a specific task to refine the
specification. For example, although rules establishment can not occur before agent specification, once
the rules are outlined it may be necessary to step back
and refine the agent specifications. Additionally, empirical evidence, case studies, and theory should drive
the model development. This information is used to
set agent specifications, environmental specifications,
and rules establishment. Quantitative data collected in
case studies interviews should be used to set the initial
parameters of the model. When historical data are
available the model should incorporate this known
history to set model parameters (variables and constants). More often than not, data are not available, in
which case parameters should be set based on qualitative models or case studies. Specifications can be theory based but should try to incorporate reality whenever possible. Additionally, as stressed by Sterman
(2000), model validation and testing should be conducted in conjunction with the case-study partners or
those decision-makers whose input guided the model
development. This guidance will usually result in refinements to the specifications. Model development
utilizing the six steps and a case study are detailed next.
Case Study
The model drove the type of case studies to be conducted and the type of information to be gathered.
For this example, once the research question was
identified and the model outlined, case studies on
the research and development strategies of several
U.S.-based electronics manufacturers were conducted. Four senior managers involved in new product
development within different industries were interviewed to ascertain the strategies they utilize in allocating resources to different project types. The model
described in this study is based on one of those firms,
a highly successful U.S. consumer electronics manufacturer. This firm went public in the summer of 2003
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387
and introduces approximately 30 new products annually, which comprise a mix of incremental and innovative products. The vice president of software development explains, ‘‘[These products include] some refreshes of existing products, some new for us, but
similar to other competitive products in the industry,
some new product ideas, [and] some new technology
introductions. I always allocate new product development dollars to new products in all of these categories.’’ Customer demand is fundamental in driving
allocation decisions. The vice president further elaborates, ‘‘Customer demand in terms of sales can help
focus our attention on products that are popular and,
therefore, I should look at ways to keep that product
line fresh and expanding. Customer feedback can help
in the tweaking and refreshing of products.’’
In this industry, product life cycles are short (one to
five years), as technology is continually evolving and
competition is intense. Product demands are seasonal,
with sales peaking during the winter holiday months.
Although customer demand strongly influences resource allocation decisions, it is often difficult to understand fully the needs and wants of the customer.
Market demand is dynamic; desire for variety, fads,
competitive offers, and demographics strongly drive
consumer choice. The innovation strategy guidelines
of this consumer electronics manufacturer are used to
examine the research question.
Step 1: Theory Operationalization/Cognitive
Map Creation
In this simplified model manufacturers compete for
customers by manufacturing and selling two different
types of products: innovative and incremental. Based
on a simplification of theory, the customers are set to
have either a preference for incremental products—
late adopters (LA)—or a preference for innovative
products—early adopters (EA). Early adopters seek
only innovative products and late adopters seek only
incremental products. Incremental products are defined as the outcome of exploitation projects, and innovative products are the outcome of exploration
projects. The goal of each manufacturer is to determine an innovation strategy, which is the percentage
of resources allocated to exploration and exploitation
activities (mix of research projects and development
projects) in order to optimize performance. As with
the case firm, this mix is driven by customer demand.
These allocations will be explained in detail in the
rules establishment section.
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R. GARCIA
Model programming even modestly complex systems often begins with some type of diagramming task
usually taken in the form of a flow diagram or logic
chart. To communicate the important details of interactions, environments, and goals of ABMs, an
expression method beyond simple flow or logic
diagrams is desired. Cognitive mapping, one such
method, is defined as a process composed of a series
of psychological transformations by which an individual acquires, codes, stores, recalls, and decodes information about the relative locations and attributes
of phenomena in their everyday spatial environment
(Downs and Stea, 1973). This can be loosely defined
as the making of a mental map. This type of mental
map is just one of many possible variations of expressing the holistic concepts of a particular ABM.
Environmental conditions pertinent to the states of
the individual agents also should be laid out on the
cognitive map.
Figure 1 demonstrates such a map for this example.
The two different types of agents, consumers and
manufacturers, are indicated with rectangles. The
Early
Adopter
ovals within the rectangles represent key variables or
attributes of the agents. The numbers express the sequence in which the variable is evaluated, set, or calculated. This model follows four general steps: (1) the
manufacturer manufactures the mix of products set
by the customer demand; (2) consumers purchase
products based on their orientation as early or late
adopters; (3) sales lead to firm performance, the primary parameter under observation; and (4) if the
manufacturer does not produce enough products to
meet the customers demand, the manufacturer conducts a reevaluation of its innovation strategy to realign the resource mix with the market demand. The
next iteration then restarts with step 1 to initiate the
next period. In this example, each iteration refers to a
quarter of a year.
Competition between the two manufacturers for
the same consumer base results in a coevolution of
firm strategies, because the innovation strategy of one
manufacturer indirectly affects the resulting performance of the second manufacturer. For example, if one
changes its allocations to 100% research activities in
Research
Stock (1)
Research
Resources
Research
Sales (2)
Performance (3)
Allocation
adjustment (4)
Development
Sales (2)
Late
Adopter
Development
Stock (1)
Development
resources
Manufacturer1
BUYERTYPE
CONSUMER
Early
Adopter
Research
Stock (1)
Research
Resources
Research
Sales (2)
Performance (3)
Late
Adopter
Allocation
adjustment (4)
Development
Sales (2)
Development
Stock (1)
Development
resources
Manufacturer2
Figure 1. Cognitive Map of Exploration/Exploitation Agent-Based Model
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
order to produce only innovative products, this leaves
an opportunity for the other manufacturer to increase
its market share for incremental innovations, which
are no longer produced by manufacturer1. Hence,
manufacturer2 should shift its resource allocations to
more development activities.
Step 2: Agent Specification
Agents are the individuals populating the simulated
environment. These agents, either in aggregate or as
individuals, are the units of analysis. Each agent has
internal states (e.g., characteristics such as degree of
connectedness to other agents, early or late adopter,
risk aversion), some that are fixed for the agent’s life
and others that change through interactions with other agents or with the external environment. Internal
states should be based on theory or collected data.
This model involves two categories of agents: manufacturers and consumers. Consumers buy products
from manufacturers based on a set constant demand.
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389
They have a single characteristic, either early or late
adopters, and are randomly determined to be early
adopters. In this model, a Netlogo slider labeled
‘‘%-early-adopters’’ is used so that the user can exogenously sets the percentage of EA to LA (see Figure
2). Netlogo uses sliders as a quick way to change a
variable without having to recode the variable every
time the program is run. For demonstration purposes,
in this example this percentage is set equal to 50:50 so
that neither innovation strategy will favor the other in
regard to consumer demand.
A manufacturer’s defining characteristic is its innovation strategy. This strategy determines the percentage of resources allocated to research, pr, and the
percentage of resources allocated to development,
pd, where pr þ pd 5 1. The variable pr is initially set in
each simulation run and is allowed to vary contingent
on the manufacturer’s performance. A manufacturer’s
state is defined by whether it is research focused
( pr4pd) or whether they are developmentally focused
(pd4pr). It is assumed that an exploration focus
Figure 2. Netlogo Model available at http://igimresearch.cba.neu.edu/netlogo/jpim
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results in innovative products attractive to early
adopters and that an exploitation focus results in
incremental products attractive to late adopters. A
50:50 ratio of resource allocation assumes that the
manufacturer equally allocates resources to both innovative and incremental products. In this model,
each manufacturer starts out with this 50:50 allocation, which will change (unless otherwise noted) as the
model progresses, since the goal of each manufacturer
is to optimize performance to meet an unknown consumer demand for incremental and innovative products. How these allocations change is described in the
section on rules specification.
Step 3: Environmental Specification
The environments of interest for innovation studies
vary. Defining the environment requires understanding the boundaries within which the agents will interact. Boundaries are usually spatial but can also be
temporal. For example, in the diffusion of innovations agents can only interact with other agents in
their network and then only once within a set period
of time. In networked ABMs, environments are frequently modeled where agents of a similar type are
spatially connected to neighbors in a torus (donutshaped). In a two-dimensional space, the agent space
looks like a lattice, but in a three-dimensional space,
agents on the edge of a lattice interact with their
neighbors on the other side of the lattice, forming a
donut-shape. However, the environment can be any
abstract structure, even one whose geometry changes
over time, such as in a scale-free network (Barabási,
2002). Environments can be defined as closed systems,
where there are no exogenous influences, or as open
systems, which may be altered by events outside the
system. As the model in this example is not spatially
dependent, other studies should be examined that do
consider spatial proximity and network effects in the
innovation process, such as Nyblom et al. (2003) and
Young (1999), and in general Albert and Barabási
(2002). Thus, for this model, the only environmental
specification defined is that consumers can only interact with manufacturers by buying products from
them, and consumers do not interact with other consumers in any manner.
Step 4: Rules Specification
Rules of behavior must be set for the agents and for
the environment. Agent movement, agent interac-
R. GARCIA
tions, and state changes depend on the behavior rules
of the agents and the rules of the system set by the
programmer. These rules should be based on theoretical suppositions, cases studies, or other experiential
data. For example, in a diffusion model a simple rule
for an agent may be to query its eight neighbors about
their individual preference for a product and then
make an informed decision whether to adopt the same
innovation. Such a rule couples the agents to other
agents, called an agent–agent rule (Epstein and Axtell,
1996). Other types of rules are agent–environment
or environment–environment, which define how the
agents will interact with each other and their environment. Rules are not limited to spatial constraints but
can be temporally set or even based on behavioral
characteristics of the agents. Rules often model company policies or doctrines that are carried out by the
individual employee. Rules of behavior can be extremely simple or as complex as the programmer
wishes to define them. However, as rules become
more complex, understanding the outcomes of the
model also becomes difficult. The rules established for
this example are based on a combination of theory
and case-study input as previously described. In order
to illustrate the basics of agent-based modeling, this
model is kept very simple. However, other exogenous
variables, such as dynamic customer demand, technology shifts, or competitive growth, can be easily
added to this model. For examples of these types of
contingencies see Garcia et al. (2004).
Consumer rules. In this example, consumers are
simple agents that follow a single rule. Every quarter
consumers buy 1 unit of their preferred product type.
Consumer Rule (C): Based on buying preference
(EA or LA), purchase 1 unit of product from a randomly selected manufacturer if it has inventory of the
preferred product.
These purchases result in RSales, unit sales of innovative products, and DSales, unit sales of incremental
products, which will differ for each manufacturer and
for each iteration (buying period) based on marketplace competitiveness. Selection is based on randomness only for the sake of simplicity.
Stochastics are introduced in the system when the
consumer selects a manufacturer as a random-number
generator scaled to the number of factories is used
to pick a manufacturer. The generator is then rescaled to eliminate the first manufacturer picked,
and a second manufacturer is selected. Inventory
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
levels are checked for the two randomly selected manufacturers, and a buying decision is made. If both
manufacturers have inventory, the selection is again
random. It is always best to start with a simple deterministic model (e.g., two competitors) when stochastics are involved. This allows the programmer to
understand the model while minimizing the randomness of the results. A two-manufacturer environment
and a 10-manufacturer environment are tested for this
model. The industry examined in the case study seldom has more than two competing products on the
shelf for this type of product line, yet how the system
reacts in a highly competitive environment is also of
interest so both types of environments are tested.
Manufacturer rules. Factories also only follow one
rule, although not so simple; they set their innovation
strategy to fit consumer demand. In allocating
resources to innovation projects, there are limited
resources. Typically limited resources, bucket, , are
available for both research and development. In this
model, changes with the success or failure of the
manufacturer in the marketspace; how these changes
occur are discussed in the next section. is initially set
equal to the number of consumers divided by the
number of manufacturers populating the marketspace
such that 5 customer population/manufacturer
population. See Table 2 for the initial settings for all
constants for each run.
Although in this model competitive strategies are
entirely based on availability of product to meet con-
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391
sumer demand, other competitive constraints such as
price or product innovativeness could have been added but were not for sake of simplicity. See Garcia et al.
(2004) for a more sophisticated model.
Resource allocation between the two different types
of projects is dependent upon the manufacturer’s
innovation strategy, pr. This variable is used to determine the percentage of resources allocated to research
activities, R, and the percentage of resources allocated
to development activities, D, such that
ð1Þ
R ¼ pr b and D ¼ ð1 pr Þ b:
The variable R represents resources to exploration
activities, and D represents resources to exploitation
activities.
Those resources allocated to research projects can
result in innovative products inventory, RStock, and
resources allocated to development projects can result
in incremental products inventory, DStock. In concordance with Levinthal and March (1981), outcomes
of research projects are modeled to be variant. Historically, research projects are inherently more risky
than development projects but have higher returns
compared to less risky development activities. To simulate the uncertainty in outcomes inherent in the different types of projects, the outcomes from research
are modeled as
ð2Þ
RStock ¼ MaxðrandðNormalðR; k1 ÞÞ; 0Þ;
where k1 represents research risk standard deviation.
Each iteration RStock is set to the greater of the randomly determined value from this distribution and zero
Table 2. Initial Settings and Results of Simulationa
Control Manufacturer Strategy
Scenario 1
Scenario 2
Scenario 3
Scenario 4
100% Explore
100% Exploit
100% Explore
100% Exploit
500
50:50
2
100%
500
50:50
2
0%
500
50:50
10
100%
500
50:50
10
0%
375
10%
375
10%
75
10%
75
10%
10%
20
10%
20
10%
20
10%
20
b
Consumer population
%Early-Adoptersb (EA:LA)
Manufacturer-populationb
pr-control; %-research
(control manufacturer only)
, resources
k1; Research-risk standard
deviationb
k2; organizational adaptivenessb
k3; product price
Outcomes
Control
Manuf.
Manuf 2
Control
Manuf.
Manuf
2–10
Control
Manuf.
Manuf 2
Control
Manuf.
Manuf
2–10
Average Market Share
Maximum Market Share
27.1%
27.4%
73.0 %
73.2%
26.9%
27.6%
73.1%
73.3%
5.79%
5.90%
10.5%
11.0%
5.58%
5.67%
10.4%
10.9%
a
b
All models were run 10 times with 100 iterations each run. The values here represent the average of these 10 runs.
Notates a slider in Netlogo model.
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R. GARCIA
so that RStock can never be less than zero. Normal(R,
k1) represents a normal distribution with mean equal to
R, the value of resources allocated to exploration activities, and a standard deviation equal to k1. As k1 increases, the distribution of the variance increases so
there is greater probability of higher successes but also
the greater possibility of more failures. Research risk
standard deviation, k1, varies from 0–100, and although it should be firm dependent, in this case it is
set constant across all manufacturers. For this example, k1 is set equal to 10 based on input from the casestudy firm. Historical data should be collected from the
case firm whenever possible in order to set constants
such as k1. Although the normal distribution is used
here, any type of random distribution can be used.
The outcomes from development are
DStock ¼ D:
ð3Þ
This models the more certain outcome of exploitation
projects; all resources allocated to exploitation activities result in incremental products. The resulting inventory of both types of products is sold to consumer
agents as previously described in the consumer rule.
Every quarter (iteration) the manufacturer will
evaluate its stocking shortage for products. Shortages are determined by the number of times a consumer
selected the manufacturer from which to buy a product and instead found no inventory available. There
may be shortages of innovative products, noRP, and
shortages of incremental products, noDP. If demand
has been met for both products (noRP 5 noDP 5 0),
there are no changes in the innovation strategy of the
manufacturer, or in other words, pr and pd do not
change. If the shortages for innovative products compared to total sales (in %) is greater than the percent
shortage for incremental products, where (noRP/
RSales)4(noDP/DSales), the manufacturer will increase its percentage of resources allocated to research
so that pr 5 pr þ k2. If (noRP/RSales)o(noDP/
DSales), the manufacturer will decrease its percentage of resources allocated to research so that
pr 5 pr k2. The variable k2 represents the degree of
organizational adaptiveness. A high value of k2 indicates a high degree of organizational flexibility to
change with marketspace conditions, and low k2
represents inflexibility. In the baseline model it is set
to 10%. This setting shifts the %-research allocation,
pr, in 10% increments in the necessary direction to
meet consumer demand. Again, this number is based
on the case study where only small incremental changes can be made quarterly by the electronic manufacturer studied. Managers in this industry confirmed
that this was a reasonable incremental change. Thus,
the greater the adaptiveness of the manufacturers, the
greater the shift in strategy. RSales and DSales are
total unit sales of each type of product during a particular iteration. Thus, in simple terms the manufacturer rule is as follows:
Manufacturer Rule (M): If noRP or noDP6¼ 0 adjust
pr; otherwise keep it the same.
The complete manufacturer agent state transition
rules are delineated in Table 3.
Step 5: Measurements Recording
Once the agents, the environment, and the rules have
been set, it is important to define what results are to be
recorded. The measurements recorded should be driven by the research question. Since the goal of using
ABMs is to discover or examine emergent or aggregate behavior, data outcomes are usually globally
measured. These global measurements guide sensitivity analysis testing as well as troubleshoot the model.
As with most computer simulations, it is important to
understand if the model has resulted in a surprising
phenomenon or if the surprise is the outcome of programming error. Proper measurement recording will
Table 3. Manufacturer Agent State Transitions
Condition
Market Condition
State
’
noRP 5 0 and noDP 5 0
no shortage of product
no change
’
noRP 5 0 and noDP40
no shortage of innovative products
shortage of incremental products
decrease pr
’
noRP40 and noDP 5 0
shortage of innovative products
no shortage of incremental products
increase pr
noRP40 and noDP40
— if %noRP/RSales4%noDP/DSales
— if %noRP/RSaleso%noDP/DSales
shortage for both product types
greater demand for innovative products
greater demand for incremental products
’
increase pr
decrease pr
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
help with this task. Data generated by the computer
program should be recorded and then evaluated with
standard statistical packages such as SPSS (2003) or
Microsoft Excel.
Determining which data to graph is also important
at this stage, as these graphs will help the researcher to
understand the model outcome. Run-time visualization of output can be very useful, which is why many
ABMs are programmed in Java with its web-based
graphical interface. Having a graphical strategy can
also greatly ease debugging the program and interpreting any surprising results of the model. Graphing
the results of a control agent also helps with troubleshooting. Here, a manufacturer is randomly selected
to take a particular state that is constant throughout
the simulation. This allows one to compare strategies
between the control manufacturer and the other adaptive manufacturer agents.
In this example, since the interest is in performance
optimization, quarter profitability and quarterly market share are recorded each iteration. Additionally,
each manufacturer’s innovation strategies, pr and pd,
are tracked quarterly (each iteration) and recorded.
Performance is determined by calculating each manufacturer’s net profit from sales using
Performance ¼ k3 ðRSales þ DSalesÞ ðR þ DÞ; ð4Þ
where R and D are defined in equation 1. RSales and
DSales have previously been defined as sales of each
product type. The variable k3 is the average product
price, which was set at US$20 here based on the case
study. More sophisticated models could set separate
prices for RSales and DSales. Performance and market share for each manufacturer are graphically presented in real time (see Figure 2).
Step 6: Run Time Specification
Another consideration is how many iterations should
be conducted within a run and then how many runs are
needed to understand the functioning of the system. An
iteration is considered as a point in time when each and
every agent’s code or rule set is computed once. The
sequencing of multiple iterations marks the passage of
virtual time as determined by the programmer. In an
iteration, each agent considers its own state, its neighbors’ states, and its environment before deciding to
change its own state. Agent state changes can be programmed to occur synchronously (simultaneous) or
asynchronously (sequential). For synchronous state
changes, all of the agents’ codes are computed before
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393
any of their states can change. For asynchronous state
changes, each agent’s state may change as its individual
code is computed without waiting for any other agent.
In the case of adopting an innovation, this can greatly
impact the diffusion results. In this example, the iterations were asynchronously (sequentially) run since no
networks were involved.
A run is a complete set of iterations. The number of
iterations in a run should be long enough for the
model to illustrate a defining state or trend that the
programmer is investigating (e.g., 100% adoption,
equilibrium reached) or set period of time is reached
(e.g., 12 weeks, 10 years). The number of runs conducted can vary significantly depending on the model
outcomes but should be driven by sensitivity analysis.
If the same results continuously occur during sensitivity analysis testing, fewer runs may be required
than when results are incongruent. Most researchers
perform 25–1000 runs with anywhere from 100 to
10,000 iterations in each run. Computational speed
often limits the number of iterations.
In this model, four different scenarios are investigated: (1) an oligopolistic environment of two manufacturers where the control manufacturer takes a
100% exploration strategy such that pr 5 1 and the
second manufacturer varies its strategy; (2) an
oligopolistic environment of two firms where the control manufacturer takes a 100% exploitation strategy
such that pr 5 0 and the other manufacturer varies its
strategy; (3) a competitive environment with 10 firms
and a constant pr 5 1 for only the control manufacturer; and (4) a competitive environment with 10 firms
and a constant pr 5 0 for only the control manufacturer. Sensitivity analyses showed that equilibrium
was reached within 100 iterations. Thus, each scenario was run 10 times with 100 iterations in each run.
The results over the 10 runs were averaged together
and are reported at the bottom of Table 2.
Translation to a Software Program
Numerous software programs exist for modeling
ABMs (Appendix A). Netlogo 2.0.2 (Wilensky,
2004), used in this study, provides a tool for any manager–researcher—not just those with computer programming skills—to build an ABM.2 An important
feature of this software is its sliders and switches.
Sliders are used in models as a quick way to change a
2
For a tutorial on Netlogo, refer to the website noted in the references.
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variable without having to recode the procedure every
time. Instead, the user moves the slider to a value and
observes what happens in the model. Switches are a
visual representation for true–false variables. The
switches can be turned on to add complexity or, in
other words, to add contingencies to the model. By
setting the sliders to predetermined values and by setting the switches to the off position, a baseline model
can be established.
Netlogo also comes with a way of easily running
‘‘what-if ’’ scenarios with BehaviorSpace—a tool that
allows one to perform experiments with the programmed model.3 This feature automatically runs a
model many times, systematically varying the slider
and switch values, or settings—a process called parameter sweeping or sensitivity analysis—to run experiments over a designated range of values. This process
allows exploration of the model’s space of possible
behaviors and helps determine which combinations of
settings or types of configurations under which the
model best performs in the multidimensional parameter space defined. The results of each simulation run
are recorded in a Microsoft Excel file, allowing for
future statistical analysis. This feature, coupled with
any statistical package, provides for an extremely effective modeling technique for this complex issue. It
also allows better troubleshooting of the model, since
interesting outcomes can be more easily traced to
emergent phenomena or to just a programming error.
Results
The results of the four different scenarios are reported
in the bottom portion of Table 2 as an example of the
type of data that is obtainable. In these scenarios, the
major question addressed is whether a manufacturer
realizes suboptimal performance in the marketspace
if it allocates 100% of its resources to exploration
activities or 100% of its resources to exploitation
as suggested by Levinthal and March (1981). A control manufacturer, whose strategy is set to a constant
exploration pr throughout the iterations, is established. The other factories will strive to align their innovation strategy to marketspace demand, which has
been set at 50%EA/50%LA. The goal of the noncontrol factories is to maximize its fitness level by building products to match marketspace demands; thus,
resources must be allocated appropriately to achieve
3
For more information on how to use BehaviorSpace, refer to the
Tool menu of any Netlogo model.
R. GARCIA
this goal. In other words, these manufacturers will
allocate 50% of their resources to exploration activities and 50% of their resources to exploitation activities in order to meet the 50:50 consumer demands.
In scenario 1, there are two competing manufacturers with the control manufacturer taking an all-explore strategy ( pr 5 1), and in scenario 2 there are two
competing firms with the control manufacturer taking
the all-exploit strategy ( pr 5 0). The two-factory marketspace demonstrates that the control manufacturer
cannot achieve performances greater than manufacturer2. Similar results are seen for both scenario 1 and
2; the control manufacturer is able to maintain a market share of approximately 27%, substantially lower
than manufacturer2’s 73%. Although it does not have
the highest performance of the two firms, it does not
fail in this marketspace. In scenario 3, the control
manufacturer was set at pr 5 1 with 10 manufacturers,
and in scenario 4 the control manufacturer was set at
pr 5 0 with 10 manufacturers in the marketspace. Similar results again are observed between these two scenarios; the control manufacturer who maintains a
constant strategy is able to maintain an average
market share of just below 6%. In this highly competitive environment, the best any manufacturer can
do is about an 11% market share, so the myopic
strategy of the control manufacturer does not lead
to performance substantially worse than the other
manufacturers.
Thus, no support is found for Levinthal and
March’s (1981) theory that firms must balance their
resources between the two different types of innovation activities in order to survive in the marketplace.
These results do align with Hannan and Freeman’s
(1977) idea of structural inertia. Organizations can
respond relatively slowly to the threats and opportunities in their environment and still remain profitable.
In this model, nonadaptive manufacturers can perform on par with adaptive manufacturers, even in
highly competitive environments, if it locates a niche
market.
Risk Variance as a Contingency
Figure 3 shows the impact on the outcomes of varying
research-risk standard deviation, k1, and competitive
intensity. Research variance is set as both a switch and
a slider in the Netlogo model (see Figure 2). By turning the switch on, the model includes variability to
the outcomes of research activities. When the switch
is off, there is no variability in outcomes; whatever
USES OF AGENT-BASED MODELING IN INNOVATION/NEW PRODUCT DEVELOPMENT RESEARCH
Control Manufacturer - 100% Exploration
35
30
Market Share
25
k1=0; n=2
k1=50; n=2
k1=100; n=2
k1=0; n=10
k1=50; n=10
k1=100; n=10
20
15
10
5
0
1
11
21
31
41
51
61
71
81
91
Time (iterations)
k1 represents research risk-standard deviation
n represents number of manufacturers in marketspace
Figure 3. Effects of Risk and Competition on Market Share for
Control Firm
resources are allocated to exploration activities results
in stock of innovative products. When the switch is
on, the slider can be set from 0–100 to vary the standard deviation of the outcomes from exploration activities. This is an example of how a contingency can
be added to a model.
The model illustrates an interesting relationship
exists between risk and competitive intensity. In the
10-factory environment, very similar results are observed in a stable environment (no variance, k1 5 0) to
an unstable environment where k1 5 100. The average
market share when k1 5 0 is 5.4% and when k1 5 100
is 5.9%. However, in the two-factory environment
manufacturers taking on risky research projects reap
the rewards. As Figure 3 shows, the best performance
occurs when the risk is highest. The safe projects lead
to stable but significantly lower market share. In
highly competitive environments, risk-taking behavior is not always the best strategy.
Model Summary
To summarize, this agent-based model simulated an
environment in which firms competed for different
types of consumers based on a simplistic innovation
strategy of manufacturing either incremental (devel-
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opment) and/or innovative (research) products. Recalling that ABMs are useful as a learning tool to
guide intuition, the results of this model can be generalized for other similar types of firms. The present
study’s model showed that a firm can be profitable in
highly competitive environments by not solely focusing on customer demands but instead by finding and
building products for a niche. Although this is not a
new insight, it does suggest that the Levinthal and
March (1981) theory regarding exploration and exploitation should be further developed for contingent
environments. The present article’s findings do lend
support to Hannan and Freeman’s (1977) concept of
structural inertia. Firms that slowly evolve over time
can do so without significantly hurting their performance outcomes. By taking a holistic approach to
the exploration–exploitation dilemma, it has been
observed that adaptiveness will lead to performance
optimization, but it is not critical that the response be
immediate. This model is presented as an example of
how agent-based modeling techniques can be used as
a managerial tool. It should be taken as an illustration
of uses of ABMs in examining innovation/new product research issues.
Future Research and Limitations with ABM
The use of ABMs in business applications is still in its
infancy. In this article, only three potential areas of
research were touched upon: diffusion of innovations,
organizational strategy, and knowledge/information
flows. There are numerous other possibilities for how
ABMs can be advantageous for studying the innovation process. This tutorial hopes to act as a catalyst
for others to investigate its uses in their own research.
Bonabeau (2002) cites three areas where limitations
or caveats exist with ABMs. Foremost, in business
applications, agents are influenced by their social context or what others around them do. These interactions cannot always easily be modeled to emulate
reality. ‘‘In social sciences, ABMs most often involve
human agents, with potentially irrational behavior,
subjective choices, and complex psychology—in other
words, soft factors, difficult to quantify, calibrate, and
sometimes justify . . . For example, a manager can
understand her marketplace better by playing with
an agent-based model of it. Then, of course, quantifying the tangible benefits of something intangible is
difficult, and a manager cannot claim to have saved
$X million by playing with a simulation of her
396
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2005;22:380–398
customers. Still, there is a lot of value in using social
simulation in a business context’’ (Bonabeau, 2002,
p. 7285).
Second, quantitative outcomes of a simulation
should be interpreted primarily at the qualitative level. There have been attempts to predict consumer behavior using ABMs. Farrell (1998) tried to predict
how (and when) such movies as Titantic or Blair
Witch Project become hits, but their model was not
very successful. Understanding the factors that lead to
a blockbuster movie is a better use of ABMs instead
of predicting when one will occur. This follows likewise with using ABMs for prediction in business applications. Prediction may be more successful once
calibration techniques have matured for ABMs. There
have been a few successful attempts at predicting human behavior in other social sciences. (See Rauch
[2002] for a few examples.) However, due to the immaturity of business models and the ‘‘varying degree
of accuracy and completeness in the input of the model (data, expertise, etc.), the nature of the output is
similarly varied, ranging from purely qualitative
insights all the way to quantitative results usable for
decision-making and implementation’’ (Bonabeau,
2002, p. 7282). But they are not yet proven prediction models.
Lastly, Bonabeau (2002) declares that, by definition, ABM looks at a system not only at the aggregate
level but also at the level of it constituent units. Although the macrolevel can be described with just a few
equations, the microlevel description involves describing the heterogeneous behavior of potentially many
individual units. Simulating the behavior of individual
agents can be extremely computationally intensive
and therefore time consuming. The high computational requirements of ABM remain a problem when
it comes to modeling large systems. Different software
programs try to remedy this problem, but those that
execute faster are limited on the graphical user interfaces available. Speed versus functionality is a tradeoff that modelers must currently make.
As previously noted, agent-based models are not
predictive models and should be used to guide intuition. Although some managers will see this as a limitation to the usefulness of this methodology, agentbased models can have an important and distinctive
place in the methodological tool kit of the innovation
researchers, either in academia or industry. It appears
as if industry has recognized the usefulness of ABMs
(Anthes, 2003) sooner than academics. The opportunities for better understanding the role of ABMs in
R. GARCIA
innovation research is in its infancy and should prove
to be a fascinating journey.
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Appendix A. Additional Sources of Information
Sources of Information on Complex Adaptive Systems and Agent-Based Modeling
The Santa Fe Institute
http://www.santafe.edu/
New England Complex Systems Institute
http://necsi.org/
International Network for Social Network Analysis (INSNA)
http://www.sfu.ca/insna/
University of Michigan’s Center for the Study of Complex Systems
http://www.cscs.umich.edu
NSF-IGERT program with University of Michigan combines complex systems studies with other disciplines
(e.g., economics, political science)
http://cscs.umich.edu/ideas-igert/
Agent-based computational economics (ACE) is the computational study of economics modeled as evolving
systems as autonomous interacting agents. ACE is thus a specialization to economics of the basic complex
adaptive systems paradigm.
http://www.econ.iastate.edu/tesfatsi/ace.htm
ISCORE researches the effects of network structure on cooperative dynamics.
http://www.fss.uu.nl/soc/iscore/papers.htm
Software/ABM Programs
Ascape
http://jasss.soc.surrey.ac.uk/4/1/5.html
RePast/Sugarscape (REcursive Porous Agent Simulation Toolkit)
http://repast.sourceforge.net/
SWARM
http://www.swarm.org/
Netlogo
http://ccl.northwestern.edu/netlogo