Modeling Strategy Switches in Multi-attribute Decision Making

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.