Reconstructing the Bayesian Adaptive Toolbox: Challenges of a dynamic environment and partial information acquisition


We show how dynamic (changing) environments can affect choice behavior, and highlight the challenges that recent models face in explaining the learning and selection of heuristic strategies under such conditions, especially when decisions are made using only a small subset of the available information. We propose an enhanced modeling framework that includes a trial-by-trial implementation of a Bayesian adaptive toolbox, redefinition of heuristic strategies, and incorporation of intricate learning rate mechanisms into a strategy learning model. We use data from a new empirical study to show how this improves the quality of inference.

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