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Bayesian Analysis of Linear Contracts

Published: 07 July 2023 Publication History

Abstract

We study a generalization of both the classic single-dimensional mechanism design problem, and the hidden-action principal-agent problem of contract theory [c.f., Alon et al. 2021]. In this setting, the principal seeks to incentivize an agent with a private Bayesian type to take a costly action. The goal is to design an incentive compatible menu of contracts which maximizes the expected revenue.

Cited By

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  • (2024)Are Bounded Contracts Learnable and Approximately Optimal?Proceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673483(315-344)Online publication date: 8-Jul-2024
  • (2024)Approximating Principal-Agent Problem Under BayesianFrontiers of Algorithmics10.1007/978-981-97-7752-5_15(185-198)Online publication date: 29-Dec-2024

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cover image ACM Conferences
EC '23: Proceedings of the 24th ACM Conference on Economics and Computation
July 2023
1253 pages
ISBN:9798400701047
DOI:10.1145/3580507
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2023

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Author Tags

  1. contract theory
  2. simple vs optimal contracts
  3. approximation

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  • Extended-abstract

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EC '23
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EC '23: 24th ACM Conference on Economics and Computation
July 9 - 12, 2023
London, United Kingdom

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

View all
  • (2024)Are Bounded Contracts Learnable and Approximately Optimal?Proceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673483(315-344)Online publication date: 8-Jul-2024
  • (2024)Approximating Principal-Agent Problem Under BayesianFrontiers of Algorithmics10.1007/978-981-97-7752-5_15(185-198)Online publication date: 29-Dec-2024

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