Computational principal-agent problems
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 co...
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| Format: | Article |
| Language: | English |
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The Econometric Society
2020
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| Online Access: | https://hdl.handle.net/1721.1/126895 |
| _version_ | 1811076629971075072 |
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| author | Azar, Pablo Daniel Micali, Silvio |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Azar, Pablo Daniel Micali, Silvio |
| author_sort | Azar, Pablo Daniel |
| collection | MIT |
| description | 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 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. |
| first_indexed | 2024-09-23T10:25:09Z |
| format | Article |
| id | mit-1721.1/126895 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:25:09Z |
| publishDate | 2020 |
| publisher | The Econometric Society |
| record_format | dspace |
| spelling | mit-1721.1/1268952022-09-30T20:59:19Z Computational principal-agent problems Azar, Pablo Daniel Micali, Silvio Massachusetts Institute of Technology. Department of Economics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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 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. Robert Solow Fellowship Stanley and Rhoda Fischer Fellowship 2020-09-02T22:59:50Z 2020-09-02T22:59:50Z 2018-05 2017-08 2019-06-14T18:19:09Z Article http://purl.org/eprint/type/JournalArticle 1555-7561 https://hdl.handle.net/1721.1/126895 Azar, Pablo D. and Silvio Micali, "Computational principal–agent problems." Theoretical Economics 13, 2 (May 2018): p. 553-78 doi. 10.3982/TE1815 ©2018 Authors en https://dx.doi.org/10.3982/TE1815 Theoretical Economics Creative Commons Attribution-NonCommercial-NoDerivs License https://creativecommons.org/licenses/by-nc/4.0/ application/pdf The Econometric Society Wiley |
| spellingShingle | Azar, Pablo Daniel Micali, Silvio Computational principal-agent problems |
| title | Computational principal-agent problems |
| title_full | Computational principal-agent problems |
| title_fullStr | Computational principal-agent problems |
| title_full_unstemmed | Computational principal-agent problems |
| title_short | Computational principal-agent problems |
| title_sort | computational principal agent problems |
| url | https://hdl.handle.net/1721.1/126895 |
| work_keys_str_mv | AT azarpablodaniel computationalprincipalagentproblems AT micalisilvio computationalprincipalagentproblems |