Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/197156 
Year of Publication: 
2018
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 13 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2018 [Pages:] 553-578
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Collecting and processing large amounts of data is becoming increasingly crucialin 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 xi of x - incentivize the agent to report the correct value f(x)with accuracy ε. complexity.
Subjects: 
Principal agent problems
computational complexity
JEL: 
D82
D86
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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