Agent-based modeling in managerial science: an illustrative survey and study

Friederike Wall

doi:10.1007/s11846-014-0139-3

Abstract

This article provides an overview of the current state of agent-based modeling in managerial science. In particular, the aim is to illustrate major lines of development in agent-based modeling in the field and to highlight the opportunities and limitations of this research approach. The article employs a twofold approach: First, a survey on research efforts employing agent-based simulation models related to domains of managerial science is given which have benefited considerably from this research method. Second, an illustrative study is conducted in the area of management accounting research, a domain which, so far, has rarely seen agent-based modeling efforts. In particular, we introduce an agent-based model that allows to investigate the relation between intra-firm interdependencies, performance measures used in incentive schemes, and accounting accuracy. We compare this model to a study which uses both, a principal-agent model and an empirical analysis. We find that the three approaches come to similar major findings but that they suffer from rather different limitations and also provide different perspectives on the subject. In particular, it becomes obvious that agent-based modeling allows us to capture complex organizational structures and provides insights into the processual features of the system under investigation.

Highlights

In the last two decades a new approach of research in the social sciences has emerged: agent-based modeling (ABM)—often synonymously termed as agentbased computational models, agent-based simulations, multi-agent systems or multi-agent simulations (e.g. Squazzoni 2010)
We found that a considerable number of articles which apparently employ ABM in managerial science and which are published in well-known scientific management journals do not use terms like ‘‘agent-based model’’ or ‘‘agent-based simulation’’ in the title, keywords or abstract— or not even in the entire text (e.g., Denrell and March 2001; Ethiraj and Levinthal 2004; Knudsen and Levinthal 2007)
We focus on the domains of strategic management, innovation, and organizational structuring and design—where each of which has seen a vast number of studies employing ABM.5

Summary

Introduction

In the last two decades a new approach of research in the social sciences has emerged: agent-based modeling (ABM)—often synonymously termed as agentbased computational models, agent-based simulations, multi-agent systems or multi-agent simulations (e.g. Squazzoni 2010). Agent-based simulation in the social sciences can serve several purposes like, for example, predicting consequences, performing certain tasks (which is typically the case in the domain of artificial intelligence), or discovering theory (Axelrod 1997a, b). The latter means that simulation is used to develop structural insights and gain understanding of fundamental processes within a certain area of interest (e.g. Davis et al 2007; Dooley 2002; Harrison et al 2007; Gilbert and Troitzsch 2005). Ostrom (1988) regards simulation as a third symbol system, apart from natural language and mathematics, for representing and communicating (theoretical) ideas; in particular, he argues that any theory which can be formulated in either mathematics or natural language can be expressed by the means of a programming language

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