Computational principal agent problems

Pablo Azar and Silvio Micali ()
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Silvio Micali: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Theoretical Economics, 2018, vol. 13, issue 2

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.

Keywords: Principal agent problems; computational complexity (search for similar items in EconPapers)
JEL-codes: D82 D86 (search for similar items in EconPapers)
Date: 2018-05-29
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Citations: View citations in EconPapers (1)

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