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. Most phenomena relevant to current organizations entail uncertainty and complexity. Understanding and managing the intrinsic complexity in organizations requires strategies that go beyond intuition (Bonabeau, 2003) and traditional analytical methods (Andriani and Mckelvey, 2007). Therefore, new theoretical and methodological frameworks are needed. Over the past two decades, agent-based simulation (ABS) has emerged as a new research and management paradigm within organizational theory (Wall, 2016). However, ABS has not been as widely adopted in management as in other domains, leaving its potential to manage organizations far from realized. We believe that this is in part due to a lack of knowledge about what has been done in the field or what are the most promising areas for future application. Hence, the goal of this paper is to provide a comprehensive survey of the literature about ABS in business and management. Such overview contributes to the establishment of a dialogue among scholars and the consolidation of ABS as a field of research in management and organizational studies. To help demarcate this field, we use the term agent-based organizational simulation (ABOS) to refer to any of the applications of ABS in business and management.