Explaining Human Decision Making in Optimal Stopping Tasks

AbstractIn an optimal stopping problem, people encounter a sequence of options and are tasked with choosing the best one; once an option is rejected, it is no longer available. Recent studies of optimal stopping suggest that people compare the current option with an internal threshold and accept it when the option exceeds the threshold. In contrast, we propose that humans decide to accept or reject an option based on an estimate of the probability that a better option will be observed in the future. We develop a computational model that formalizes this idea, and compare the model to the optimal policy in two experiments. Our model provides a better account of the data than the optimal model. In particular, our model explains how the distributional structure of option values affects stopping behavior, providing a step towards a more complete psychological theory of optimal stopping.

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