An elementary mechanism for simultaneously modeling discrete decisions and decision times

Abstract

In the field of system dynamics (SD), there has been a missing set of theoretically sound techniques for explicitly modeling dynamics during discrete decision-making processes across varying levels and types of decision pressures. Purchasing a property, filing a divorce, approving a merger, imposing a tariff, and launching a war are examples of actions that have broader ramifications; in these cases, the decisions and timing of those decisions are crucial in understanding and predicting the interactions between the decision-makers and their environments. Sequential Sampling Models (SSMs) have remained commonplace in cognitive psychology (CP) for decades because of their utility in simultaneously capturing individual decisions and decision-time distributions. This article reviews existing SSM literature and proposes a generalized, elementary mechanism distilled from existing SSMs, which establishes a connection between SD and CP in the hope of benefiting both fields.