Efficiently learning from revealed preference

Authors:
Morteza Zadimoghaddam;Aaron Roth
Affiliations:
MIT, CSAIL;University of Pennsylvania
Venue:
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Year:
2012

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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.