Decision factors that support preference learning


People routinely draw inferences about others' preferences by observing their decisions. We study these inferences by characterizing a space of simple observed decisions. Previous work on attribution theory has identified several factors that predict whether a given decision provides strong evidence for an underlying preference. We identify one additional factor and show that a simple probabilistic model captures all of these factors. The model goes beyond verbal formulations of attribution theory by generating quantitative predictions about the full set of decisions that we consider. We test some of these predictions in two experiments: one with decisions involving positive effects and one with decisions involving negative effects. The second experiment confirms that inferences vary in systematic ways when positive effects are replaced by negative effects.

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