Publications

     

The effects of goals and evironments on human performance in optimal stopping problems

M. Guan and M. D. Lee In optimal stopping problems, people are asked to choose the best option out of a sequence of alternatives, under the constraint that they cannot return to an earlier option once it is rejected. We examine human performance on variations of the optimal stopping problem, with different environments and with different goals. Specifically, we consider environments that have relatively high or low numbers, under the goals of choosing the maximum or the minimum. A natural consequence of this design is that we study the decisions that people make in both congruent situations, in which the environment and goal align, and incongruent situations, in which the environment and goal differ. First, we present empirical evidence that people adapt to both high and low environments as well as to both maximum and minimum goal frames, and that they make decisions consistent with using threshold-based models. Second, we apply a previously developed threshold model of individual performance to our data, inferring the thresholds people use. Lastly, we use Bayes factors to test whether people are sensitive to environments, goals, and congruency in optimal stopping problems. Overall, our results suggest that there are psychological similarities in congruent situations, pointing toward invariances and rationalities in the way people solve optimal stopping problems as the environments and goals change, without contaminating framing effects. DOI: 10.1037/dec0000081

Citation

Guan, M., & Lee, M. D. (2018). The effects of goals and evironments on human performance in optimal stopping problems. Decision, 5, 339–361.

BibTeX

@article{guan2017effects,
  title     = {The effects of goals and evironments on human performance in optimal stopping problems},
  author    = {Guan, M. and Lee, M. D.},
  journal   = {Decision},
  year      = {2018},
  volume    = {5},
  pages     = {339--361},
  doi       = {10.1037/dec0000081},
}