0, the principal can— by learning a single component x1 of x—incentivize the agent to report the correct value f(x) with accuracy ε."> 0, the principal can— by learning a single component x1 of x—incentivize the agent to report the correct value f(x) with accuracy ε.">
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Computational principal agent problems

Author

Listed:
  • Azar, Pablo D.

    (Department of Economics, Massachusetts Institute of Technology)

  • Micali, Silvio

    (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology)

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 x1 of x—incentivize the agent to report the correct value f(x) with accuracy ε.

Suggested Citation

  • Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
    • Handle: RePEc:the:publsh:1815
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Principal agent problems; computational complexity;

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law

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