Evaluating the inverse decision-making approach to preference learning

author: Alan Jern, Carnegie Mellon University
published: Sept. 6, 2012,   recorded: December 2011,   views: 2472


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Psychologists have recently begun to develop computational accounts of how people infer others' preferences from their behavior. The inverse decision-making approach proposes that people infer preferences by inverting a generative model of decision-making. Existing data sets, however, do not provide sufficient resolution to thoroughly evaluate this approach. We introduce a new preference learning task that provides a benchmark for evaluating computational accounts and use it to compare the inverse decision-making approach to a feature-based approach, which relies on a discriminative combination of decision features. Our data support the inverse decision-making approach to preference learning.

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