Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2019
Abstract
Research and design competitions aim to promote innovation or creative production, which are often best achieved through collaboration. The nature of a competition, however, typically necessitates sorting by individual performance. This presents tradeoffs for the competition designer, between incentivizing global performance and distinguishing individual capability. We model this situation in terms of an abstract collaboration game, where individual effort also benefits neighboring agents. We propose a scoring mechanism called LSWM that rewards agents based on localized social welfare. We show that LSWM promotes global performance, in that social optima are equilibria of the mechanism. Moreover, we establish conditions under which the mechanism leads to increased collaboration, and under which it ensures a formally defined distinguishability property. Through experiments, we evaluate the degree of distinguishability achieved whether or not the theoretical conditions identified hold.
Keywords
collaboration incentives, mechanism design, scoring competitions
Discipline
Computer and Systems Architecture
Research Areas
Data Science and Engineering
Publication
Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), Montreal, Canada, May 13-17
First Page
556
Last Page
564
Identifier
10.5555/3306127.3331740
City or Country
Montreal, Canada
Citation
SINHA, Arunesh and WELLMAN, Michael P..
Incentivizing collaboration in a competition. (2019). Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), Montreal, Canada, May 13-17. 556-564.
Available at: https://ink.library.smu.edu.sg/sis_research/4796
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5555/3306127.3331740