Abstract
Purpose The purpose of this paper is to provide a comprehensive survey of the literature about the use of agent-based simulation (ABS) in the study of organizational behavior, decision making, and problem-solving. It aims at contributing to the consolidation of ABS as a field of applied research in management and organizational studies.
Design/methodology/approach The authors carried out a non-systematic search in literature published between 2000 and 2016, by using the keyword “agent-based” to search through Scopus’ business, management and accounting database. Additional search criteria were devised using the papers’ keywords and the categories defined by the divisions and interest groups of the Academy of Management. The authors found 181 articles for this survey.
Findings The survey shows that ABS provides a robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving. The authors show that the areas that report the highest number of applications are operations and logistics (37 percent), marketing (17 percent) and organizational behavior (14 percent).
Originality/value The paper illustrates the increasingly prominent role of ABS in fields such as organizational behavior, strategy, human resources, marketing and logistics. To-date, this is the most complete survey about ABS in all management areas.
1. Introduction
Today’s markets and organizations are complex systems (CS). CS are made up of heterogeneous elements that interact with each other and the environment, generating interdependencies across multiple spatial and temporal scales that are difficult to understand, predict and control (Boisot and Child, 1999). A distinctive feature of CS is their ability to exhibit complex emergent properties, i.e. counterintuitive aggregate properties (Mitchell, 2009). Their environment is characterized by dynamic, fast-paced changes across different domains that make them prone to uncertainty, systemic risks and networked effects (Helbing, 2013). To be able to cope, organizations must create mechanisms to learn, adapt and coevolve under such circumstances.
