Preference Elicitation for Participatory Budgeting

• 成果类型: Article
• 署名作者: Benade, Gerdus; Nath, Swaprava; Procaccia, Ariel D.; Shah, Nisarg
• 署名单位: Boston University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Harvard University; University of Toronto
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3666
• 发表日期: 2021
• 页码: 2813-2827
• 关键词: Group decisions; voting committees; utility preference theory; Artificial intelligence

摘要

Participatory budgeting enables the allocation of public funds by collecting and aggregating individual preferences. It has already had a sizable real-world impact, but making the most of this new paradigm requires rethinking some of the basics of computational social choice, including the very way in which individuals express their preferences. We attempt to maximize social welfare by using observed votes as proxies for voters' unknown underlying utilities, and analytically compare four preference elicitation methods: knapsack votes, rankings by value or value for money, and threshold approval votes. We find that threshold approval voting is qualitatively superior, and also performs well in experiments using data from real participatory budgeting elections.