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
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 Îµ > 0, the principal canâ€” by learning a single component x1 of xâ€”incentivize the agent to report the correct value f(x) with accuracy Îµ.
Keywords: Principal agent problems; computational complexity (search for similar items in EconPapers)
JEL-codes: D82 D86 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:the:publsh:1815
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