Abstract
We study how to incentivize agents in a target sub-population 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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 |
| Editors | Luc De Raedt, Luc De Raedt |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 279-285 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781956792003 |
| DOIs | |
| State | Published - 2022 |
| Event | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria Duration: Jul 23 2022 → Jul 29 2022 |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| ISSN (Print) | 1045-0823 |
Conference
| Conference | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 |
|---|---|
| Country/Territory | Austria |
| City | Vienna |
| Period | 7/23/22 → 7/29/22 |
Funding
We would like to thank the reviewers for their valuable feedback. The second author is supported by Clarendon Fund and SKP Scholarship.
ASJC Scopus subject areas
- Artificial Intelligence