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dc.contributor.authorAzar, Pablo Daniel
dc.contributor.authorMicali, Silvio
dc.date.accessioned2020-09-02T22:59:50Z
dc.date.available2020-09-02T22:59:50Z
dc.date.issued2018-05
dc.date.submitted2017-08
dc.identifier.issn1555-7561
dc.identifier.urihttps://hdl.handle.net/1721.1/126895
dc.description.abstractCollecting 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 xi of x—incentivize the agent to report the correct value f(x) with accuracy ϵ. complexity. Copyright ©2018 The Authors.en_US
dc.description.sponsorshipRobert Solow Fellowshipen_US
dc.description.sponsorshipStanley and Rhoda Fischer Fellowshipen_US
dc.language.isoen
dc.publisherThe Econometric Societyen_US
dc.relation.isversionofhttps://dx.doi.org/10.3982/TE1815en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceWileyen_US
dc.titleComputational principal-agent problemsen_US
dc.typeArticleen_US
dc.identifier.citationAzar, Pablo D. and Silvio Micali, "Computational principal–agent problems." Theoretical Economics 13, 2 (May 2018): p. 553-78 doi. 10.3982/TE1815 ©2018 Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Economicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalTheoretical Economicsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-06-14T18:19:09Z
dspace.date.submission2019-06-14T18:19:10Z
mit.journal.volume13en_US
mit.journal.issue2en_US
mit.metadata.statusComplete


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