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Evaluating computational models of preference learning
Alan Jern · Christopher G Lucas · Charles Kemp

Tue Dec 13 01:52 AM -- 01:56 AM (PST) @ None

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.

Author Information

Alan Jern (Rose-Hulman Institute of Technology)
Christopher G Lucas (University of Edinburgh)
Charles Kemp (Carnegie Mellon University)

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