# Decision factors that support preference learning

- Alan Jern,
*Carnegie Mellon University*
- Charles Kemp,
*Carnegie Mellon University*

## Abstract

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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