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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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. https://doi.org/10.1007/978-3-642-35311-6_9
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DOI: https://doi.org/10.1007/978-3-642-35311-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35310-9
Online ISBN: 978-3-642-35311-6
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