Preference Identification

Abstract

An experimenter seeks to learn a subject's preference relation. The experimenter produces pairs of alternatives. For each pair, the subject is asked to choose. We argue that, in general, large but finite data do not give close approximations of the subject's preference, even when countably infinite many data points are enough to infer the preference perfectly. We then provide sufficient conditions on the set of alternatives, preferences, and sequences of pairs so that the observation of finitely many choices allows the experimenter to learn the subject's preference with arbitrary precision. The sufficient conditions are strong, but encompass many situations of interest. And while preferences are approximated, we show that it is harder to identify utility functions. We illustrate our results with several examples, including expected utility, and preferences in the Anscombe-Aumann model.

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