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Modeling Strategy Switches in Multi-attribute Decision Making

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Abstract

We develop and demonstrate a method for inferring changes in strategy use, applicable to decision making in multi-attribute choice. The method is an extension of one developed by Lee, Gluck, and Walsh (Decision 6:335–368, 2019) and continues to rely on a Bayesian approach for inferring strategy switches based on spike-and-slab priors. The extensions improve the existing method in two ways. The first is by using a hierarchical approach to make inferences about the underlying propensity to switch strategies simultaneously at both the individual and group levels. The second is by making inferences about the probability different strategies are used, including the transition probabilities between strategies when switches are made. We demonstrate the method by applying it to data sets from five previous experiments, involving a range of experimental designs and sets of strategies of interest. We conclude by discussing the potential of the method to contribute to addressing basic questions in human decision making involving the nature of adaptation, learning, and self-regulation.

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Notes

  1. We also observed a change in the performance of the sampler JAGS uses for categorical distributions moving from version 4.2 to 4.3, which led to much slower computational performance in the newer version for the current code and data.

  2. We thank Thorsten Pachur for raising this potential limitation.

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Acknowledgments

We thank Alex Hough, Reilly Innes, and an anonymous reviewer for helpful comments. MDL’s collaboration was enabled through an appointment to the Oak Ridge Institute for Science and Education (ORISE) Faculty Research Program.

Funding

This research was supported by the U. S. Air Force Research Laboratory’s 711th Human Performance Wing, through the Personalized Learning and Readiness Sciences Core Research Area.

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Authors and Affiliations

  1. Department of Cognitive Sciences, University of California, Irvine, CA, USA

    Michael D. Lee

  2. 711 Human Performance Wing, Air Force Research Laboratory, Wright-Patterson AFB, Dayton, OH, USA

    Kevin A. Gluck

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  1. Michael D. Lee
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  2. Kevin A. Gluck
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Correspondence to Michael D. Lee.

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Data and Code Availability

Code and data for all of the analyses presented in this article are available in a github repository at https://github.com/mdlee/switchingStrategies.

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The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government.

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Lee, M.D., Gluck, K.A. Modeling Strategy Switches in Multi-attribute Decision Making. Comput Brain Behav 4, 148–163 (2021). https://doi.org/10.1007/s42113-020-00092-w

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