Contests to Incentivize a Target Group

Contests to Incentivize a Target Group

Edith Elkind, Abheek Ghosh, Paul W. Goldberg

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 279-285. https://doi.org/10.24963/ijcai.2022/40

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.
Keywords:
Agent-based and Multi-agent Systems: Mechanism Design
Agent-based and Multi-agent Systems: Algorithmic Game Theory
Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems
AI Ethics, Trust, Fairness: Fairness & Diversity
Multidisciplinary Topics and Applications: Economics