Abstract
Organizations are driven by social interactions such as decision processes, negotiations or operations. Those interactions are composed of multiple simultaneous, dynamically evolving processes with several different agents. However, researchers in the field of management science traditionally focus on aggregated characteristics and assume equilibria, thus correctly neglecting the level of individual agents. Thus, most research methods in management science are based on cross-sectional data as well as stable and predictable events.
Recently, modeling and simulation methods are becoming increasingly accepted among management scientists in order to better cope with complex problems and to better capture the underlying processes of social interactions.
In this paper, we present simulation as an appropriate research method to better handle complexity within this field. In particular, we present two distinct simulation methods: Agent-based modeling and system dynamics. We discuss the value and use of simulation models for supporting theory building and testing in management science. Further, we discuss the prerequisits, advantages and challenges of simulation methods.
Aside of general advantages that any simulation method offers, we also point to differences between the two simulation methods. In summary, we advocate a stronger use of simulation as additional research method in management science because it may improve the reliability and soundness of existing theories by focusing on the social interactions which are drivers of most business processes. At the same time, we emphasize the need for in-depth methodological knowledge and a thorough understanding of adequacy of the simulation method for the problem under investigation.
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