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Fairness and Incentive Compatibility via Percentage Fees

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

    2 Scopus citations

    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
    PublisherAssociation for Computing Machinery, Inc
    Pages517-535
    Number of pages19
    ISBN (Electronic)9798400701047
    DOIs
    StatePublished - 9 Jul 2023
    Event24th ACM Conference on Economics and Computation, EC 2023 - London, United Kingdom
    Duration: 9 Jul 202312 Jul 2023

    Publication series

    NameEC 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
    Period9/07/2312/07/23

    ASJC Scopus subject areas

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

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