Preference Elicitation for Participatory Budgeting

Author:

Benadè Gerdus1ORCID,Nath Swaprava2ORCID,Procaccia Ariel D.3,Shah Nisarg4

Affiliation:

1. Questrom School of Business, Boston University, Boston, Massachusetts 02215;

2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016 Kanpur, India;

3. School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138;

4. Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada

Abstract

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. This paper was accepted by Yan Chen, decision analysis.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

Reference35 articles.

1. Abramowitz B, Anshelevich E (2018) Utilitarians without utilities: Maximizing social welfare for graph problems using only ordinal preferences. Proc. 32nd AAAI Conf. Artificial Intelligence (AAAI, Palo Alto, CA), 894–901.

2. Anshelevich E, Postl J (2016) Randomized social choice functions under metric preferences. Proc. 25th Internat. Joint Conf. Artificial Intelligence (IJCAI, Pasadena, CA), 46–52.

3. Anshelevich E, Sekar S (2016) Blind, greedy, and random: Algorithms for matching and clustering using only ordinal information. Proc. 30th AAAI Conf. Artificial Intelligence (AAAI, Palo Alto, CA), 390–396.

4. Anshelevich E, Bhardwaj O, Postl J (2015) Approximating optimal social choice under metric preferences. Proc. 29th AAAI Conf. Artificial Intelligence (AAAI, Palo Alto, CA), 777–783.

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1. Optimized Distortion and Proportional Fairness in Voting;ACM Transactions on Economics and Computation;2024-01-19

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