Understanding the Complexity of Simple Decisions: Modeling Multiple Behaviors and Switching Strategies


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Abstract

We develop models of strategy use in multiple-cue decision making that have a couple of key capabilities. The first is that they incorporate multiple sources of behavior, using the predictions that strategies make about processes and outcomes as a basis for inferring strategy use. The second is that they allow for people to change strategies multiple times over a sequence of decision trials. The models are implemented as generative probabilistic models, allowing for fully Bayesian inference about the nature of strategy use and the number of strategy switches. To demonstrate the approach and evaluate the models, we consider the standard take-the-best, weighted-additive, and tally strategies, as well as a guessing strategy, and apply them to previously published experimental data that involve decision, search, and verbal report data (Walsh & Gluck, 2016). We find strong evidence that many people switch strategies many times, especially when inference is based on all of the available behavioral data. Our results suggest there is interpretable complexity beneath people’s use of simple strategies to make decisions.

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