From Monopoly to Competition: Optimal Contests Prevail

Xiaotie Deng, Yotam Gafni, Ron Lavi, Tao Lin, Hongyi Ling

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)

Abstract

We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design and symmetric contestants. The goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form an equilibrium in the model with competition among contests. Under a very natural assumption these contests are in fact dominant, and the equilibria that they form are unique. Moreover, equilibria with the optimal contests are Pareto-optimal even in cases where other equilibria emerge. In many natural cases, they also maximize the social welfare.

Original languageEnglish
Title of host publicationProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
EditorsBrian Williams, Yiling Chen, Jennifer Neville
Place of PublicationU. S. A.
PublisherAAAI Press
Pages5608-5615
Number of pages8
ISBN (Electronic)9781577358800
Publication statusPublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, USA United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume37

Conference

Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUSA United States
CityWashington
Period7/02/2314/02/23

Bibliographical note

Funding Information:
This work was partially supported by the NSFC-ISF joint research program (grant No. NSFC-ISF 61761146005). Yotam Gafni was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant No. 740435).

Funding Information:
Yotam Gafni was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant No. 740435).

ASJC Scopus subject areas

  • Artificial Intelligence

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