Modeling optimal stopping in changing environments: A case study in mate selection

Optimal stopping problems require people to choose from a sequence of values presented sequentially, under the constraint that it is not possible to return to an earlier option. Usually, the distribution from which values are drawn is the same for each option in the sequence. We consider an extension in which the distributions change in a known way over options. Based on an experimental task involving mate selection, we study people’s optimal stopping behavior in two different changing environments. Basic empirical results, and a cognitive modeling analysis, find evidence that people use relatively simple cognitive strategies to set internal thresholds that guide their decision-making. Using the model-based analysis, we reach some conclusions about the nature of individual difference in strategy use and the optimality of the thresholds people use. In particular, we find that while people are sensitive to …

See the article at Computational Brain & Behavior