Evaluating the inverse decision-making approach to preference learning

Alan Jern, Christopher G. Lucas, Charles Kemp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 24
EditorsJ. Shawe-Taylor, R.S. Zemel, P. Bartlett, F.C.N. Pereira, K.Q. Weinberger
Pages2276-2284
Number of pages9
Publication statusPublished - 2011

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