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Computational principal-agent problems

Author(s)
Azar, Pablo Daniel; Micali, Silvio
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Creative Commons Attribution-NonCommercial-NoDerivs License https://creativecommons.org/licenses/by-nc/4.0/
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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.
Date issued
2018-05
URI
https://hdl.handle.net/1721.1/126895
Department
Massachusetts Institute of Technology. Department of Economics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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
ISSN
1555-7561

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