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Abstract:
One key question is whether people rely on frugal heuristics orfull-informationstrategieswhen making preference decisions. We propose a novel method,model-based activelearning, to answer whether people conform more to a rank-based heuristic(Take-The-Best) or a weight-based full-information strategy (logistic regression). Ourmethod eclipses traditional model comparison techniques by using information theory tocharacterize model predictions for how decision makers should actively sample information.These analyses capture how sampling affects learning and how learning affects decisions onsubsequent trials. We develop and test model-based active learning algorithms for bothTake-The-Best and logistic regression. Our findings reveal that people largely follow aweight-based learning strategy rather than a rank-based strategy, even in cases where theirpreference decisions are better predicted by the Take-The-Best heuristic. This findingsuggests that people may have more refined knowledge than is revealed by their preferencedecisions, but which can be revealed by their information sampling behavior. We arguethat model-based active learning is an effective and sensitive method for model selectionthat expands the basis for model comparison.
