Fairness and Incentive Compatibility via Percentage Fees

Shahar Dobzinski, Sigal Oren, Jan Vondrak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

We study incentive-compatible mechanisms that maximize the Nash Social Welfare. Since traditional incentive-compatible mechanisms cannot maximize the Nash Social Welfare even approximately, we propose changing the traditional model. Inspired by a widely used charging method (e.g., royalties, a lawyer that charges some percentage of possible future compensation), we suggest charging the players some percentage of their value of the outcome. We call this model the percentage fee model.We show that there is a mechanism that maximizes exactly the Nash Social Welfare in every setting with non-negative valuations. Moreover, we prove an analog of Roberts theorem that essentially says that if the valuations are non-negative, then the only implementable social choice functions are those that maximize weighted variants of the Nash Social Welfare. We develop polynomial time incentive compatible approximation algorithms for the Nash Social Welfare with subadditive valuations and prove some hardness results.

Original languageEnglish
Title of host publicationEC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
Pages517-535
Number of pages19
ISBN (Electronic)9798400701047
DOIs
Publication statusPublished - 9 Jul 2023
Event24th ACM Conference on Economics and Computation, EC 2023 - London, United Kingdom
Duration: 9 Jul 202312 Jul 2023

Publication series

SeriesEC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation

Conference

Conference24th ACM Conference on Economics and Computation, EC 2023
Country/TerritoryUnited Kingdom
CityLondon
Period9/7/2312/7/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Computational Mathematics
  • Statistics and Probability

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