文章基本信息

标题：Random-Subset Voting
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作者：Guilherme Barros Correa de Amorim ; Ana Paula Cabral Seixas Costa ; Danielle Costa Morais 等
期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2018
卷号：21
期号：1
页码：1-17
DOI：10.18564/jasss.3610
出版社：University of Surrey, Department of Sociology
摘要：Most of the voting procedures in the literature assume that voters have complete and transitive preferences over the alternatives. A basic premise is that any voter is able to evaluate any pair of alternatives in a set and select his/her preferred one, or indicate indierence between them. Nevertheless, some researchers have highlighted that voters, as humans, have limited capacity to deal with and consequently compare big sets of alternatives. In this paper, we propose the Random-Subset Voting, a voting procedure that through a random approach allows the voters to evaluate less alternatives. Instead of analyzing the entire set of alternatives, each voter will evaluate a random subset of a pre-determined size. We have proposed a theorem indicating that, for large sets of voters, the outcomes of traditional Borda and Random-Subset Borda converge. We have also implemented a web experiment and a Monte Carlo simulation in order to validate the proposed procedure and analyze how it behaves in several scenarios.
关键词：Decision Theory; Voting Procedure; Random Subsets; Borda; Bounded Rationality