Computational principal-agent problems
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Azar_et_al-2018-Theoretical_Economics.pdf
Description
Published version
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245.22 KB
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Adobe PDF
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Author(s)
Azar, Pablo Daniel
Micali, Silvio
Date Issued
May 2018
Journal
Theoretical Economics
Publisher
The Econometric Society
Citation
Azar, Pablo D. and Silvio Micali, "Computational principal–agent problems." Theoretical Economics 13, 2 (May 2018): p. 553-78 doi. 10.3982/TE1815 ©2018 Authors
Version
Final published version
Abstract
Collecting and processing large amounts of data is becoming increasingly crucial in our society. We model this task as evaluating a function f over a large vector x=(x1,…,xn), which is unknown, but drawn from a publicly known distribution X. In our model, learning each component of the input x is costly, but computing the output f(x) has zero cost once x is known. We consider the problem of a principal who wishes to delegate the evaluation of f to an agent whose cost of learning any number of components of x is always lower than the corresponding cost of the principal. We prove that, for every continuous function f and every ϵ>0, the principal can—by learning a single component xi of x—incentivize the agent to report the correct value f(x) with accuracy ϵ. complexity. Copyright ©2018 The Authors.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.3982/TE1815
