Computational Analysis of Firms' Organization and Strategic Behaviour

Edoardo Mollona

• Editore: Routledge
• Anno: 2010
• ISBN:
  9781136959943
• Edizione: 1
• Argomento: Business
• Categoria: Business General

This book addresses possible applications of computer simulation to theory building in management and organizational theory. The key hypothesis is that modelling and computer simulation provide an environment to develop, test and articulate theoretical propositions. In general, computer simulation provides an experimental environment where researchers are able to play with symbolic representations of phenomena by modifying the model's structure and activating or deactivating model's parameters. This environment allows to both generating hypotheses to ex post explain observed phenomena or to ex ante generate distributions of unrealized events thereby envisioning areas for further empirical investigations.

Under a methodological perspective, the volume investigates logics and techniques to design a research strategy grounded on computer simulation. In particular, the articles in the book concentrate on two different techniques, and philosophies, to set up a simulation study: System Dynamics, which is grounded on differential equations and feedback theory, and agent-based modeling. The book describes how computer simulation helps to look into research issues typical to strategic management and organizational theory. In this respect, such themes as firms' diversification strategies, competitive strategy, rivalry and the impact of role dynamics on organizational performances are explored through the lenses of computer simulation models.

Part I
Why and How

Using Computer Simulation for Theory Development in Social Sciences

1
The Use of Computer Simulation in Strategy and Organization Research

Edoardo Mollona

INTRODUCTION

With different fortunes and oscillating enthusiasm, computer simulation has supported theoretical investigation in managerial disciplines since the 1960s. In the attempt to further corroborate the role of computer simulation in the repertoire of research strategies available to social scientists, the aim of the present chapter is twofold. First, I would like to describe the logic underpinning the adoption of computer simulation in management and organization research. Thus, I propose a historical journey into a selection of contributions to speculate on the different logics of inquiry that permeate these studies. Second, I sketch out the framework for a research strategy that combines computer simulation and field-based inquiry.

To begin with, it is important to set up in the front a definition for computer simulation. Computer simulation has to do with the manipulation of symbols using a computer code; more specifically, it uses algorithms to derive propositions from the assumptions that come together in a computer model. A computer model is a formal model in which ‘[ … ] the implications of the assumptions, that is, the conclusions, are derived by allowing an electronic digital computer to simulate the processes embodied in the assumptions’ (Cohen and Cyert 1961: 115).

In this respect, computer models can be regarded as special cases of mathematical models (Cohen and Cyert 1961) in which conclusions are derived from assumptions by using a computer simulation rather than a process of analytical solution. On the other hand, however, computer models do not necessarily have to be stated in mathematical and numerical form (Clarkson and Simon 1960) since they allow manipulation of symbols that can be words, phrases and sentences. Therefore, computer models make up the subset of mathematical models that are solved numerically rather than analytically, but not all the computer models are stated in mathematical terms since they may incorporate not-mathematical symbols. In this respect, Troitzsch suggests that computer simulation is a third system beside natural language and mathematics (1998: 27).

In principle, computer simulation is just a technologically aided process of deduction. Yet, the crude technology can vary strongly from different approaches; and, more importantly, the difference in the adopted technology often unveils profound differences in the philosophy that lies beneath modeling.

For example, computer simulations based on systems of difference equations are inspired by a structuralist stance that sees the behavior of the individuals that are embedded within a social system as determined by the feedback nature of the causal relationships that characterize the system (Forrester 1958, 1961). Agent-based models or cellular automata, on the other hand, simulate actions and interactions of autonomous individual entities and build on the hypothesis that the behavior of social systems can be modeled and understood as evolving out of interacting autonomous learning agents (Epstein and Axtell 1996; Axelrod 1997; Axtell 1999). Thus, a crucial feature of these models is the emergence of ordered structures independently of top-down planning.

While agent-based models and cellular automata show how interaction among individual decision making and learning may generate complex aggregate behavior, differential equation modeling aims at reducing aggregate and often puzzling behaviors into underlying feedback causal structures. As a consequence, these latter models typically aggregate agents into a relatively small number of states, assuming their perfect mixing and homogeneity (Rahmandad and Sterman 2004) while cellular automata and, especially, agent-based models preserve heterogeneity and individual attributes thereby sacrificing parsimony. The reader looking for an overview of approaches and techniques may refer to the texts edited by, for example, Liebrand et al. (1998) or Gilbert and Troitzsch (2005).

However, independently of the approach adopted and the inspiring philosophy, research work employing computer simulation has frequently been regarded, in social sciences, as influenced by an autonomous logic in respect to mainstream research. Simulation studies, however, have a long tradition in organizational research. Going back to seminal work in the area of the behavioral theory of the firm and organizational decision theory, some of the most important theoretical pieces are based on a simulation approach. This is true, for example, for the well-known garbage can model (Cohen et al. 1972) and for the work leading to the development of the behavioral theory of the firm (Cyert et al. 1959; Cyert and March 1963).

In recent times, computer simulations have recuperated terrain in mainstream management journals. To push further legitimization of computer simulation in the study of firms’ strategy and organization, this chapter aims to capture logical underpinnings of successful simulation research.

The chapter is organized as follows: in the next section I briefly pinpoint key milestones in the history of computer simulation applied to strategy and organization research and, in the following section, the reasons that motivated early adopters to use computer simulation are summarized. In section 4, I consider a sample of recent works that use simulation and I muse on the differences in the underlying logic of inquiry. In the fifth section, I focus on a specific issue: the association of computer simulation and field research. In the last section of the chapter I draw some conclusions.

A HISTORICAL JOURNEY INTO THE ADVANCEMENT OF COMPUTER SIMULATION INTO STRATEGY AND ORGANIZATION THEORY

The use of computer simulation has intrigued social scientists having roots in a variety of cultural territories. Computer models have played a role in sociology and political science. A review conducted by Meinhart (1966) reveals that at the beginning of the 1960s a group of sociologists and politics researchers shared an enthusiasm for the use of computer simulations.

As reported by Meinhart, computer simulations supported McPhee in studying voting behavior (1961), Coe in investigating conflict in dyadic relationships (1964) and Gullahorn and Gullahorn in examining individual reactions in social interactions (1963). They followed a line of research initiated by Simon (1952) who formalized Homans’ theory of interaction (1950).

Grounding on this strong foothold, the use of computer simulation has cultivated robust roots in sociology. Beginning from Axelrod’s work (1984), for example, a well-established thread of studies explored emergence of social order and cooperation from the micro-interaction of boundedly rational individuals (Epstein and Axtell 1996; Lomborg 1996; Nettle and Dunbar 1997; Macy and Skvoretz 1998; Eguiluz et al. 2005; Hanaki et al. 2007). In different veins, another insightful example of an application of computer simulation to sociology is the analysis of theories of conflict conducted by Hanneman et al. (1995). Furthermore, the cross-fertilization between computational models and social sciences gave rise to a fertile field of studies labeled social simulation (Gilbert and Doran 1994; Gilbert and Conte 1995; Troitzsch et al. 1996; Gilbert and Troitzch 2005; Edmonds et al. 2007).

As for strategy and organization theory, my analysis moves from economics because, when computer simulation first appeared, studies in strategy and organization, under a theoretical point of view, were growing as branches of the more consolidated field of economics.

As Clarkson and Simon suggest (1960), three threads of studies have shared an interest in applying computer simulation in economics: the thread of studies on dynamic macroeconomics, the studies on operations management and the studies dealing with the theory of decision making.

The first thread employed computer simulation in the analysis of business cycles and market dynamics to deal with non-linearities and growing complexity of dynamic systems of differential equations. As the complexity of mathematical models began to increase, researchers had to hang on numerical analysis and computer simulation to explore the behavior of these systems. Here, computer simulation was employed as a technical device not as a research approach having its own logic. Indeed, in this area of macroeconomics dynamics, Clarkson and Simon provide a reference to a textbook of econometrics written by Klein (1953).

The second area of studies adopted computer simulation to test computational algorithms aimed at finding optimal decision rules in complex business decision-making situations (Bowman and Fetter 1957; Churchman et al. 1957; Vazsonyi 1958; Dorfman 1960).¹

To investigate the origin of the application of computer simulation to the study of firms’ strategy and organization, we focus on the third mentioned thread that is focused on economic decision making. To start our historical journey, we make use of the hints provided by Cohen in the paper he presented at the annual meeting of the American Economic Association in 1960 (Cohen 1960b) and in the paper published the following year with colleague Cyert (1961). They indicate a group of researchers as the pioneers of computer simulation in economics. What makes this group of scholars fairly homogenous is their shared aspiration to explore the implications of realistic representations of decision-making processes, removing the pressure to obtain mathematically tractable formalizations. This research agenda illuminated the advantage of co...