This article is concerned with the study of statistical power in agent-based modeling (ABM). After an overview of classic statistics theory on how to interpret Type-II error (whose occurrence is also referred to as a false negative) and power, the manuscript presents a study on ABM simulation articles published in management journals and other outlets likely to publish management and organizational research. Findings show that most studies are underpowered, with some being overpowered. After discussing the risks of under- and overpower, we present two formulas to approximate the number of simulation runs to reach an appropriate level of power. The study concludes with the importance for organizational behavior scholars to perform their models in an attempt to reach a power of 0.95 or higher at the 0.01 significance level.

Purpose: – This paper aims at introducing agent-based models (ABMs) and reviews some of their features in an attempt to show why they can be useful for organizational behavior research.Design/methodology/approach: – The use of simulations has increased substantially in the past ten to fifteen years, but management seems to hold back to the agent-based “revolution”. The paper first describes the ABMs, and then discusses some of the issues that usually prevent management scholars from using simulations.Findings: – This paper indicates how an agent-based approach can help overcome the hesitations surrounding computer simulations because (a) it makes it relatively easy to model emergent and complex social phenomena, and (b) simulation is made easier by user-friendly software platforms that connect it to the existing research methods.Originality/value: – This article describes ABMs in a way that may be attractive to organization scholars, and it depicts the frontiers of a more flexible computational and mathematical approach to organizations, management and teams.