The article discusses the usefulness of simulation models in business strategy research and reviews the relatively small but fast-growing literature applying simulation models (especially in the form of agent-based simulation models) to the study of organizational forms, to problems of design of organizations and technologies and to industry dynamics. Some suggestions are also provided for future directions of research.

ResearchGate Logo

Discover the world's research

  • 20+ million members
  • 135+ million publications
  • 700k+ research projects

Join for free

ESM0723 simulation modelling and business strategy

research

Definition

Simulation models provide a distinct analytical approach

from closed-form mathematical modelling. In particular, in

agent-based modelling, the agent, or micro-entity, is

characterized as well as the nature of the interaction with

the environment and other agents. The analysis consists of

characterizing the emergent behaviour from these

interactions.

Abstract

The article discusses the usefulness of simulation models in

business strategy research and reviews the relatively small

but fast-growing literature applying simulation models

(especially in the form of agent-based simulation models) to

the study of organizational forms, to problems of design of

organizations and technologies and to industry dynamics.

Some suggestions are also provided for future directions of

research.

Simulation modelling is used in a variety of

disciplines in the social and natural sciences. Why

might this approach to theory development have

particular appeal to strategy researchers? Business

strategy, at a fundamental level, hinges on issues of

spatial and temporal interdependence. With respect

to the former, since the early writings of Andrews

(1971) scholars have recognized that strategy involves

thinking of the firm in terms of part–whole relations.

The distinction between business strategy relative to

functional strategies lay in the consideration of how

choices in one functional domain (marketing, opera-

tions, human resources, finance) effected other

functional domains. In an intertemporal sense, a

choice is viewed as 'strategic' if it has consequences

for subsequent choices and payoffs. Thus, a notion of

path dependence is inherent in the notion of strategic

decision-making. Finally, more recent game-theoretic

treatments of business strategy have highlighted the

interdependence among firms and, in this light, a

choice is viewed as strategic if it influences the

choices and payoffs of other firms.

Having recognized that for a choice to be strategic

it must entail a linkage across time or space (both

within and across firms), how does this help address

our motivating question regarding the possible value

of simulation modelling? In particular, why would

simulation modelling potentially have a comparative

advantage in strategy research? Lane (1993)

highlights some of the basic properties that push a

researcher from the domain of closed form analytical

modelling efforts to more 'open' computational

approaches. First, processes which are both path-

dependent and uncertain, or stochastic, greatly

constrain the possibilities of formal modelling. This

is what motivated Bellman's famous comment

regarding the curse of dimensionality in the context

of the possible limits of dynamic programming

approaches. The standard way to address this

constraint within the tradition of analytical model-

ling, particularly traditional microeconomic

approaches, is to restrict attention to two-period

formulations of the strategy problem. There is a

period two of final outcomes and a period one in

which farsighted firms anticipate these possible future

outcomes. Such efforts can reflect reasonably well

situations of one-shot, upfront capital investments

and the like, but are poorly suited to consider more

general path dependency.

Another 'fix' to the problem of a vastly expanding

state space is to model 'representative' firms and to

repress issues of firm heterogeneity. Classic models of

industry concentration adopted this approach (Tir-

ole, 1988). However, firm heterogeneity, its basis and

implications, lie at the core of strategy research.

Strategy researchers are interested in difference in

performance among a set of firms (Rumelt et al.,

1994) and not merely contrasts of performance

across industries. Furthermore, even in Porter's

(1980) classic treatment of industry analysis, there

is considerable attention to the importance of

possible asymmetries among firms in an industry

and their implications for firm behaviour and, in

turn, industry performance.

Finally, simulation models can naturally accom-

modate more plausible behavioural assumptions on

agents than utility (or profit) optimization as

normally assumed by standard equilibrium models.

Boundedly rational heuristics, routines, limited fore-

sight and recall, adaptation, and learning can be

easily modelled by computer algorithms and made

part of larger models of firm and industry dynamics.

Thus, it is natural for strategy researchers to

explore the path dynamics of heterogeneous firms

following boundedly rational behavioural rules,

computational techniques, and Monte Carlo simula-

tion models in particular. Beginning with the

pioneering work of Cyert and March (1963) , this

modelling approach is not only a latent opportunity

ESM0723

rMacmillan Publishers Limited 1

ESM0723

but has already had a number of important

expressions, though considerable further opportu-

nities remain. In what follows, we briefly summarize

the most recent lines of research.

Organizational forms

Many of the existing efforts have focused on issues of

organizational form and their implications for

strategy and performance. Research has tackled this

issue from the angles of information processing

(Miller, 2001 ), learning and adaptation (Marengo,

1992 and 1996), and search processes ( Rivkin and

Siggelkow, 2002, 2005; Siggelkow and Levinthal,

2005; Marengo and Dosi, 2005).

Miller (2001) models organizations as tree-like

structures in which each node represents a basic

information processing capability and edges are

communication channels between the two connected

nodes. Organizations are generated by randomly

combining nodes, they adapt through mutation and

recombination, and they are selected by the environ-

ment according to their performance. Adaptation

and selection is shown to be a very powerful device

for performance improvement. The model also shows

a trade-off between the higher processing power of

larger organizations and the higher coordination

costs they imply as diminishing returns are quickly

reached with the addition of new nodes.

While Miller's model assumes that information

processing capabilities of individual agents (nodes)

are given, Marengo (1992, 1996) present models

which focus instead upon the modification of such

information processing capabilities, that is, a process

of structural learning. Individual agents are imperfect

adaptive learners, and they adaptively adjust their

information processing capabilities through trial-

and-error learning, driven by information coming

from the environment and/or from other members of

the organization. The model shows that the archi-

tecture of such information flows, and, in particular,

the degree of centralization/decentralization, plays a

crucial role in determining the learning patterns and

the performance characteristics of the organization.

Intra-organizational distribution of information is

also shown to set a balance in the exploration vs.

exploitation trade-off in organizational learning

(March, 1991).

The last few years have seen the development of a

new family of evolutionary models of organizations,

inspired by biologist Stuart Kauffman's NK model

(Kauffman, 1993 ). This line of research, fully

embracing an evolutionary perspective (Nelson and

Winter, 1982), considers organizational dynamics as

the outcome of the interaction between organiza-

tional processes of variation (i.e. creation of novelty),

selection, and retention. In large and complex (i.e.

characterized by many interdependent components)

search spaces, organizational forms implement a

decomposition or quasi-decomposition (Simon,

1969) of the search space and determine the

dynamics of the search process.

One important result of this family of models is

that the power of selection forces is limited by

variational mechanisms and therefore by the organi-

zational structure itself. Only a small fraction of the

vast and largely unknown search space is usually

generated by variation and, in turn, providing the

fodder for selection processes. This simple result

opens up plenty of opportunities for analyzing, on

the one hand, the room for strategic action and its

cognitive foundation: search is not random but is

informed by cognitive representation (Gavetti and

Levinthal, 2000) and by the division of labour

(Marengo and Dosi, 2005 ;Rivkin and Siggelkow,

2003); and, on the other hand, its limitations arising

from the complexity of diachronic and synchronic

interdependencies of strategic decisions (Ghemawat

and Levinthal, 2008). In general, the structural

properties of organizational forms can be studied

not by beginning with assumptions of market

inefficiencies as in transaction cost theory, but in

their long-term dynamic properties of supporting

effective search processes. An important emerging

line of research addresses the role of intra-organiza-

tional reward and incentive schemes in shaping the

internal selection environment and its interaction

with the external environment (Dosi, Levinthal and

Marengo, 2003; Rivkin and Siggelkow, 2003;Siggelk-

ow and Rivkin, 2005).

The design of technologies

Scholars have also examined the question of design in

terms of technological systems and, in particular, the

role of modularity (Ethiraj and Levinthal, 2004a,

2004b; Brusoni et al., 2007; Frenken 2006). Once

again the pioneering work of Herbert Simon on near

decomposability and its evolutionary properties

simulation modelling and business strategy research

2rMacmillan Publishers Limited

ESM0723

(Simon, 1969 ; Callebaut and Rasskin-Gutman, 2005)

provides the theoretical background.

It is often argued that, by adopting modular design

strategies, firms can take responsibility for the design

and development of separate modules. Thus, they can

develop new products at a faster pace, as the

integration of the final product is a matter of mix

and match of 'black boxes' (e.g. Baldwin and Clark,

2000). However, little attention has been devoted to

the costs of modularity which arise from the

inevitable suboptimality of any decomposition of a

complex system whose structure of interdependen-

cies is only partly understood by boundedly rational

agents (Ethiraj and Levinthal, 2004a, 2004b ). Sub-

optimality derives from the separation into different

modules of interdependent components and is the

outcome of a trade-off between the search for fast

performance improvements allowed by modulariza-

tion and the danger of lock-in into inferior designs or

designs which, though efficient in the short run, are

unable to evolve and adapt to environmental changes

or sustain innovation. The papers cited above define

this trade-off precisely, analyse its consequences in

environments characterized by different degrees of

uncertainty and non-stationarity, and discuss its

consequences for the design of technologies and

organizations.

Strategy and industry dynamics

Researchers have also engaged in work that links

these firm-level dynamics to broader patterns at the

industry or population level. The pioneering work of

Nelson and Winter (1982) and Winter (1984) on the

evolution of industries in various regimes of

Schumpeterian dynamics represents an important

early and still vibrant strand of work of computa-

tional approaches to industry dynamics. Links with

the previously mentioned line of research on

organizational adaptation and search have fruitfully

enriched and complemented these early works on the

Schumpeterian dynamics of industries, providing

richer microfoundations to standard industry

dynamics arguments.

Intrafirm variational mechanisms have been

shown to constitute a source of persistent hetero-

geneity among firms operating in the same industry

even in stationary environments and a source of

differential performance in changing environments

(Levinthal, 1997 ), as well as a limit to the power of

market selection forces in driving firms even to

locally optimal efficiency (Rivkin and Siggelkow,

2002).

Simulation models have been used to investigate

the strategic choices firms make between imitation

and experimentation in adapting their capabilities

and also have identified a source of inter-firm

persistent heterogeneity in the differential cost and

timing of capability deployment across firms (Zott,

2003). Lenox, Rockart and Lewin (2006), (2007)

combine agent-based models based on the NK

framework mentioned above, where firms search for

activities that complement one another, with tradi-

tional economic models of industry competition

among profit-maximizing firms with entry and exit.

Industry-level profitability, concentration, entry, exit

and shakeouts during the industry life cycle are

shown to depend crucially upon the pattern of

interdependencies at the industry level.

Finally, a promising line of research is offered by

the so-called history-friendly models, that is, simula-

tion models of a lower level of abstraction in

comparison to the ones mentioned so far, in which

the larger number of parameters are calibrated using

data suggested by in-depth analysis of the history of

real industries (see Malerba et al., 2008 , for an

application to the evolution of the computer

industry). These kinds of models could provide a

useful tool for experimenting in the lab with

alternative strategies.

In short, simulation modelling has been used to

address a number of central substantive questions in

strategic management and appears to have some

inherent competitive advantages for doing so. While

a niche 'strategy' among the array of approaches to

strategy research, the approach has made some

important contributions to date and is likely to be

an even more central approach in the future.

DANIEL A. LEVINTHAL AND LUIGI MARENGO

See also

bounded rationality; business strategy; Monte Carlo;

simulation modelling and business research strategy;

technology; theory of the firm

Uncited References

Siggelkow and AU1

Levinthal, 2003.

References

Andrews, K. 1971. The Concept of Corporate Strategy.

Homewood, IL: Richard D. Irwin.

simulation modelling and business strategy research

rMacmillan Publishers Limited 3

ESM0723

Baldwin, C.Y. and Clark, K.B. 2000. Design Rules: The Power of

Modularity. Cambridge, MA: MIT Press.

Brusoni, S., Marengo, L., Prencipe, A. and Valente, M. 2007. The

value and cost of modularity: A cognitive perspective. European

Management Review 4, 121–132.

Callebaut, W. and Rasskin-Gutman, D. eds 2005. Modularity:

Understanding the Development and Evolution of Complex

Natural Systems. Cambridge MA: MIT Press.

Cyert, R.M. and March, J.G. 1963. A Behavioral Theory of the

Firm. Englewood Cliffs, NJ: Prentice-Hall.

Dosi, G., Levinthal, D. and Marengo, L. 2003. Bridging contested

terrain: linking incentive-based and learning perspectives on

organizational evolution. Industrial and Corporate Change 12,

413–436.

Ethiraj, S.K. and Levinthal, D. 2004a. Bounded rationality and

the search for organizational architecture: an evolutionary

perspective on the design of organizations and their

evolvability. Administrative Science Quarterly 49, 404–437.

Ethiraj, S.K. and Levinthal, D. 2004b. Modularity and innovation

in complex systems, Management Science 50, 159–173.

Frenken, K. 2006. Innovation, Evolution and Complexity Theory.

Cheltenham : Edward Elgar.

Gavetti, G. and Levinthal, D. 2000. Looking forward and looking

backward: cognitive and experiential search. Administrative

Science Quarterly 45, 113–137.

Ghemawat, P. and Levinthal, D. 2008. Choice interactions and

business strategy, Management Science 54, 1638–1651.

Kauffman, S.A. 1993. The Origins of Order . Oxford: Oxford

University Press.

Lane, D. 1993. Artificial worlds in economics: Part I. Journal of

Evolutionary Economics 3, 89–107.

Lenox, M., Rockart, S. and Lewin, A. 2006. Interdependency,

competition, and the distribution of firm and industry profits.

Management Science 52, 757–772.

Lenox, M., Rockart, S. and Lewin, A. 2007. Interdependency,

competition, and industry dynamics. Management Science 53,

599–615.

Levinthal, D. 1997. Adaptation on rugged landscapes.

Management Science 43, 934–950.

Malerba, F., Nelson, R., Orsenigo, L. and Winter, S. 2008.

Vertical integration and disintegration of computer firms: a

history-friendly model of the coevolution of the computer and

semiconductor industries. Industrial and Corporate Change 17,

197–231.

March, J.G. 1991. Exploration and exploitation in organizational

learning, Organization Science 2, 71–87.

Marengo, L. 1992. Coordination and organizational learning in

the firm. Journal of Evolutionary Economics 2, 313–326.

Marengo, L. 1996. Structure, competence and learning in an

adaptive model of the firm. In Organization and Strategy in the

Evolution of the Enterprise, ed. G. Dosi and F. Malerba. London:

Macmillan.

Marengo, L. and Dosi, G. 2005. Division of labor, organizational

coordination and market mechanisms in collective problem-

solving. Journal of Economic Behavior and Organization 58,

303–326.

Miller, J.H. 2001. Evolving information processing organizations.

In Dynamics of Organizations: Computational Modeling and

Organization Theories, ed. A. Lomi and E.R. Larsen.

Cambridge, MA: MIT Press.

Nelson, R. and Winter, S.G. 1982. An Evolutionary Theory of

Economic Change. Cambridge, MA: The Belknap Press of

Harvard University Press.

Porter, M. 1980. Competitive Strategy. New York: Free Press.

Rivkin, J. and Siggelkow, N. 2002. Organizational sticking points

on NK landscapes. Complexity 7, 31–43.

Rivkin, J. and Siggelkow, N. 2003. Balancing search and stability:

Interdependencies among elements of organizational design.

Management Science 49, 290–321.

Rumelt, R., Schendel, D. and Teece, D. 1994. Fundamental Issues

in Strategy. Cambridge, MA: Harvard Business School Press.

Siggelkow, N. and Levinthal, D. 2003. Temporarily divide to

conquer: centralized, decentralized, and reintegrated

organizational approaches to exploration and adaptation.

Organization Science 14, 650–669.

Siggelkow, N. and Rivkin, J. 2005. Speed and search: designing

organizations for turbulence and complexity. Organization

Science 16, 101–122.

Simon, H. 1969. The Sciences of the Artificial . Cambridge, MA:

MIT Press.

Tirole, J. 1988. The Theory of Industrial Organization.

Cambridge, MA: MIT Press.

Winter, S.G. 1984. Scumpeterian competition in alternative

technological regimes. Journal of Economic Behavior and

Organization 5, 287–320.

Zott, C. 2003. Dynamic capabilities and the emergence of intra-

industry differential firm performance: Insights from a

simulation study. Strategic Management Journal 24, 97–125.

simulation modelling and business strategy research

4rMacmillan Publishers Limited

ESM0723

Non-Print Items

Classification: methods/methodology

Keywords

bounded rationality; business strategy; Monte Carlo; technology; simulation modelling and business strategy

research; theory of the firm

Additional index terms

simulation modelling

bounded rationality

business strategy

theory of the firm

NK framework

industry dynamics

Monte Carlo

simulation modelling

technology

technology design

ESM0723

Author Query Form

AU: 1 Please provide citations in text for these references.

... This is because it enables the design of an organisational architecture as well as specific solutions and processes used and taking place within the organisation (Wynn and Clarkson, 2018). These include those that involve undertaking and intensifying cooperation between enterprises in order to combine various business models (Wikström, Artto, Kujala and Söderlund, 2010), solving various management-related and organisational problems (Szarucki, 2013), simulating activities and directions of development that are desired in the organisation in specific circumstances or market situations (Levinthal and Marengo, 2016), optimising the functioning of enterprises (Kamrani, Ayani and Moradi, 2011) or effective risk management (Bac, 2010). For this reason, models are used on a large scale in the practice of business operations. ...

  • Krzysztof Bartczak
  • Stanislaw Lobejko Stanislaw Lobejko

Purpose: The aim of the article is to present an innovative model for measuring attitudes towards digital technology platforms. Design/Methodology/Approach: Such a model, based on a sample of 120 Polish companies, was developed as a result of research conducted in 2019. When building the model, a regression analysis of qualitative variables was applied, which involves predicting the values of specific variables. A top-down method was applied in this respect. In addition, an alternative version of the developed model was proposed. Findings: The construction of the model made it possible to prove that the factor which most strongly influences the attitudes of the management staff of Polish enterprises towards digital technology platforms is an economic factor (i.e., financial benefits associated with the use of such platforms). Furthermore, space for further research was created, including with regard to company structure, the industry in which it operates and the number of employees working there as correlates of attitudes towards digital technology platforms. Originality/value: The article discusses an innovative model for measuring attitudes towards digital technology platforms.

  • Sendil K. Ethiraj
  • Daniel A. Levinthal Daniel A. Levinthal

The problem of designing, coordinating and managing complex systems is central to the management and organizations literature. Recent writings have emphasized the important role of modularity in enhancing the adaptability of such complex systems. However, little attention has been paid to the problem of identifying what constitutes an appropriate modularization of a complex system. We develop a formal simulation model that allows us to carefully examine the dynamics of innovation and performance in complex systems. The model points to the trade-off between the virtues of parallelism that modularity offers and the destabilizing effects of overly refined modularization. In addition, high levels of integration can lead to modest levels of search and a premature fixation on inferior designs. The model captures some key aspects of technological evolution as a joint process of autonomous firm level innovation and the interaction of systems and modules in the marketplace. We discuss the implications of these arguments for product and organization design.

  • Luigi Marengo

Organizational learning [cf. for instance the pioneering work of Cyert and March (1963) and, for a broad outline of its main economic implications, Nelson and Winter (1982) is an issue which deserves primary attention when studying the dynamic performance of economic systems. But organizational learning — it will be argued in this chapter — cannot be adequately handled within the existing dominant analytical framework of economic theory. Recent attempts to accommodate organizational issues within the neoclassical theory of the firm have impressively broadened the scope of the latter and tackled fundamental questions which used to lie outside the concern of economic theory; but they have not been able to deal in a satisfactory way with the problem of learning because neoclassical theory, in these most recent developments, is concerned with information, whereas learning is about knowledge.

Coordination of interdependencies among firms' productive activities has been advanced as a promising explanation for sustained heterogeneity in capabilities among firms. In this paper, the extend this line of research to determine the industry structures and patterns of expected firm profits for the case when difficulty optimizing interdependent activities does, in fact, generate and sustain capability heterogeneity among firms. We combine a widely used agent-based model where firms search to discover sets of activities that complement one another (reducing overall costs or raising product quality) with traditional economic models of competition among profit-maximizing firms. The agent-based model produces a distribution of performance (interpreted as variable cost or product quality) among firms and the competition models determine resulting industry outcomes including patterns of entry, exit, and profits. The integration of economic models of competition among firms with an agent-based model of search for improvement by firms reveals a rich relationship between interdependencies in production functions and industry structure, firm profits, and industry average profitability.

  • Giovanni Dosi Giovanni Dosi

In this paper we present a general model of organizational problem-solving in which we explore the relationship between problem complexity, decentralization of tasks and reward schemes. When facing complex problems that require the co-ordination of large numbers of interdependent elements, organizations face a decomposition problem that has both cognitive dimensions and reward and incentive dimensions. The former relate to the decomposition and allocation of the process of generation of new solutions: since the search space is too vast to be searched extensively, organizations employ heuristics for reducing it. The decomposition heuristic takes the form of division of cognitive labour and determines which solutions are generated and become candidates for selection. The reward and incentive dimensions fundamentally shape the selection environment which chooses over alternative solutions. The model we present begins to study the interrelationships between these two domains of analysis: in particular, we compare the problem-solving performance of organizations characterized by various decompositions (of coarser or finer grain) and various reward schemes (at the level of the entire organization, team and individual). Moreover we investigate extensions of our model in order to account for (admittedly rudimentary) power and authority relationships (giving some parts of the organization the power to stop changes in other parts), and discuss the interaction of problem representations and incentive mechanisms. Copyright 2003, Oxford University Press.

  • R. M. Cyert
  • E. A. Feigenbaum
  • J. G. March

How do business organizations make decisions? What process do they follow in deciding how much to produce? And at what price? A behavioral theory of the firm is here explored. Using a specific type of duopoly, a model is written explicity as a computer program to deal with the complex theory implicit in the process by which businesses make decisions. This model highlights our need for more empirical observations of organizational decision-making.