Aspirations Levels in Agent-Based Models of Decision-Making in Organizational Contexts

Many agent-based models of human decision-making in organizations employ representations and algorithms comprising decision-makers’ aspirations. However, aspiration levels usually do not receive much attention in the modeling efforts, nor is agent-based modeling employed to understand better the effects and emergence of aspiration levels in decision-making. This paper elaborates on the relevance of aspiration levels in agent-based models using the widely used hill-climbing algorithms and reinforcement learning as examples. The paper provides a framework for the modeler’s multi-faceted design choices when capturing aspiration levels for decision-making with a particular focus on organizational contexts. The framework builds on the ODD + D protocol, which has been proposed explicitly for agent-based models with human decision-makers. The framework also allows deriving potential contributions of the agent-based modeling approach to understanding the effects of aspiration levels in organizations. These may, for example, include the dynamic interactions between individual and organizational aspirations, the adaptation to environmental changes, or the relevance of decision-makers’ cognitive capabilities.