Proceedings of the Annual Meeting of the Cognitive Science Society

Although process data indicate that people often rely on sim-plifying processes when choosing between risky options, cur-rent models of heuristics cannot predict people’s choices veryaccurately. To address this apparent paradox, it has been pro-posed that people might adaptively choose from a toolboxof simple strategies. But which strategies are contained inthis toolbox? And how do people decide when to use whichdecision strategy? Here, we develop a model according towhich the decision maker selects a decision strategy for a givenchoice problem rationally from a toolbox of strategies; the con-tent of the toolbox is estimated for each individual decisionmaker. Using cross-validation on an empirical data set, we findthat this model of strategy selection from a personal adaptivetoolbox predicts people’s choices better than any single strat-egy (even when it is allowed to vary across participants) andbetter than previously proposed toolbox models. Our modelcomparisons show that both inferring the content of the tool-box and rational strategy selection are critical for accuratelypredicting people’s risky choices. Furthermore, our analysisreveals considerable individual differences in the set of strate-gies people are equipped with and how they choose amongthem; these individual differences could partly explain whysome people make better choices than others. These findingsrepresent an important step towards a complete formalizationof the notion that people select their cognitive strategies froma personal adaptive toolbox.