Contests to Incentivize a Target Group

Edith Elkind; Abheek Ghosh; Paul W. Goldberg

IJCAI 2022poster2 citations

Contests to Incentivize a Target Group

Edith Elkind, Abheek Ghosh, Paul W. Goldberg

Abstract

We study how to incentivize agents in a target subpopulation to produce a higher output by means of rank-order allocation contests, in the context of incomplete information. We describe a symmetric Bayes--Nash equilibrium for contests that have two types of rank-based prizes: (1) prizes that are accessible only to the agents in the target group; (2) prizes that are accessible to everyone. We also specialize this equilibrium characterization to two important sub-cases: (i) contests that do not discriminate while awarding the prizes, i.e., only have prizes that are accessible to everyone; (ii) contests that have prize quotas for the groups, and each group can compete only for prizes in their share. For these models, we also study the properties of the contest that maximizes the expected total output by the agents in the target group.

Agent-based and Multi-agent Systems: Mechanism DesignAgent-based and Multi-agent Systems: Algorithmic Game TheoryAgent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based SystemsAI Ethics, Trust, Fairness: Fairness & DiversityMultidisciplinary Topics and Applications: Economics

BibTeX

@inproceedings{ijcai2022p40,
  title     = {Contests to Incentivize a Target Group},
  author    = {Elkind, Edith and Ghosh, Abheek and Goldberg, Paul W.},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {279--285},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/40},
  url       = {https://doi.org/10.24963/ijcai.2022/40},
}