Efficiently Learning from Revealed Preference

  • Morteza Zadimoghaddam
  • Aaron Roth
Conference paper

DOI: 10.1007/978-3-642-35311-6_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)
Cite this paper as:
Zadimoghaddam M., Roth A. (2012) Efficiently Learning from Revealed Preference. In: Goldberg P.W. (eds) Internet and Network Economics. WINE 2012. Lecture Notes in Computer Science, vol 7695. Springer, Berlin, Heidelberg

Abstract

In this paper, we consider the revealed preferences problem from a learning perspective. Every day, a price vector and a budget is drawn from an unknown distribution, and a rational agent buys his most preferred bundle according to some unknown utility function, subject to the given prices and budget constraint. We wish not only to find a utility function which rationalizes a finite set of observations, but to produce a hypothesis valuation function which accurately predicts the behavior of the agent in the future. We give efficient algorithms with polynomial sample-complexity for agents with linear valuation functions, as well as for agents with linearly separable, concave valuation functions with bounded second derivative.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Morteza Zadimoghaddam
    • 1
  • Aaron Roth
    • 2
  1. 1.MIT, CSAILUSA
  2. 2.University of PennsylvaniaUSA

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