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Learning Economic Parameters from Revealed Preferences

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Web and Internet Economics (WINE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8877))

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Abstract

A recent line of work, starting with Beigman and Vohra [4] and Zadimoghaddam and Roth [30], has addressed the problem of learning a utility function from revealed preference data. The goal here is to make use of past data describing the purchases of a utility maximizing agent when faced with certain prices and budget constraints in order to produce a hypothesis function that can accurately forecast the future behavior of the agent.

In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes. By drawing a connection to recent advances in multi-class learning, we provide a computationally efficient algorithm with tight sample complexity guarantees (\(\tilde{\Theta}(d/\epsilon)\) for the case of d goods) for learning linear utility functions under a linear price model. This solves an open question in Zadimoghaddam and Roth [30]. Our technique yields numerous generalizations including the ability to learn other well-studied classes of utility functions, to deal with a misspecified model, and with non-linear prices.

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Balcan, MF., Daniely, A., Mehta, R., Urner, R., Vazirani, V.V. (2014). Learning Economic Parameters from Revealed Preferences. In: Liu, TY., Qi, Q., Ye, Y. (eds) Web and Internet Economics. WINE 2014. Lecture Notes in Computer Science, vol 8877. Springer, Cham. https://doi.org/10.1007/978-3-319-13129-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-13129-0_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13128-3

  • Online ISBN: 978-3-319-13129-0

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