Theoretical Economics 13 (2018), 553–578
Computational principal agent problems
Pablo D. Azar, Silvio Micali
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
JEL classification: D82,D86
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