文章基本信息

标题：Computational principal–agent problems
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作者：Pablo D. Azar ; Silvio Micali
期刊名称：Theoretical Economics
印刷版ISSN：1555-7561
出版年度：2018
卷号：13
期号：2
页码：553-578
DOI：10.3982/TE1815
语种：English
出版社：Econometric Society
摘要：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 ϵ0, 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.