Generating Voting Rules from Random Relations
Author: 
Nic WilsonAuthors Info & Claims
Pages 2267 - 2269
Abstract
We consider a way of generating voting rules based on a random relation, the winners being alternatives that have the highest probability of being supported. We consider different notions of support, such as whether an alternative dominates the other alternatives, or whether an alternative is undominated, and we consider structural assumptions on the form of the random relation, such as being acyclic, asymmetric, connex or transitive. We give sufficient conditions on the supporting function for the associated voting rule to satisfy various properties such as Pareto and monotonicity. The random generation scheme involves a parameter p between zero and one. Further voting rules are obtained by tending p to zero, and by tending p to one, and these limiting rules satisfy a homogeneity property, and, in certain cases, Condorcet consistency. We define a language of supporting functions based on eight natural properties, and categorise the different rules that can be generated for the limiting p cases.
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Index Terms
- Generating Voting Rules from Random Relations
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Information
Published In
May 2019
2518 pages
ISBN:9781450363099
- General Chairs:
- Edith Elkind,
- Manuela Veloso,
- Program Chairs:
- Noa Agmon,
- Matthew E. Taylor
Sponsors
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 08 May 2019
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- Research-article
Funding Sources
- European Regional Development Fund
- Science Foundation Ireland
Conference
AAMAS '19
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AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
May 13 - 17, 2019
Montreal QC, Canada
Acceptance Rates
AAMAS '19 Paper Acceptance Rate 193 of 793 submissions, 24%;
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
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