A Linear Threshold Model for Optimal Stopping Behavior

Resumen

In many real life decisions, options are distributed in space and time, making it necessary to sequentially search through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision making strategies by humans vary but largely diverge from this optimal strategy; the reasons for this divergence remain unknown. We present a new model of human stopping decisions in sequential decision making tasks based on a linear threshold heuristic. We show that the new model outperforms existing models for sequential decision making. Moreover, it accurately predicts participants’ search length, and how they adapt it to different environments. It thus provides an important step towards understanding human sequential decision making.

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Cómo citar (APA 7)

Baumann, C., Gershman, S. J., Singmann, H., & von Helversen, B. (2019). A Linear Threshold Model for Optimal Stopping Behavior. https://doi.org/10.31234/osf.io/cbn6t