Abstract for: Capturing Discrete Choice and Deliberation Time Endogenously Using Sequential Sampling and Accumulation-to-Threshold Principles
Modeling the adjustment delays for perceptions has been studied comprehensively in the system dynamics (SD) literature. However, the associated decisions have been commonly assumed to be made (as if) in every model step. Therefore, there seems a missing set of theoretically-sound techniques in explicitly capturing the deliberation time during a discrete choice, which may take variable steps to trigger, regardless of whether there is any perception delay. For example, the harder a decision is perceived, the longer it may take to decide, after the deliberation is triggered. Moreover, the deliberation time distributions associated with different alternatives might vary for a given decision problem. Since new information or events might occur during the deliberation, this variation has implication on the final decision and the systemwide path-dependency. Adding even more complexity is the varying degrees of perceived pressure when deciding. On the other hand, models based on the sequential-sampling (SS) and stochastic-accumulation-to-threshold (SAT) principles have been dominant in cognitive psychology (CP) for simultaneously capturing preference and deliberation delays for decades. This paper aims at establishing a connection between SD and CP in terms of modeling stochastic discrete choices and their associated decision time distributions. Five major generalizations and their suitable application contexts are then proposed by synthesizing the techniques and wisdom from the two fields.