Contests to Incentivize a Target Group
Authors: Edith Elkind, Abheek Ghosh, Paul W. Goldberg
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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.We initiate a theoretical study of contest design to elicit higher participation by agents from under-represented groups, and propose tractable models for doing so. We analyze the equilibrium behavior of the agents for three cases... |
| Researcher Affiliation | Academia | Edith Elkind , Abheek Ghosh and Paul W. Goldberg Department of Computer Science, University of Oxford {edith.elkind,abheek.ghosh,paul.goldberg}@cs.ox.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it state that code is released. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. It refers to distributions F and G as part of its theoretical model. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, so no specific dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not specify any ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |