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Preference Elicitation for Participatory Budgeting

Author

Listed:
  • Gerdus Benadè

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Swaprava Nath

    (Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016 Kanpur, India)

  • Ariel D. Procaccia

    (School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138)

  • Nisarg Shah

    (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.

Suggested Citation

  • Gerdus Benadè & Swaprava Nath & Ariel D. Procaccia & Nisarg Shah, 2021. "Preference Elicitation for Participatory Budgeting," Management Science, INFORMS, vol. 67(5), pages 2813-2827, May.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:5:p:2813-2827
    DOI: 10.1287/mnsc.2020.3666
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    References listed on IDEAS

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    1. George B. Dantzig, 1957. "Discrete-Variable Extremum Problems," Operations Research, INFORMS, vol. 5(2), pages 266-288, April.
    2. Young, H. P., 1988. "Condorcet's Theory of Voting," American Political Science Review, Cambridge University Press, vol. 82(4), pages 1231-1244, December.
    3. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
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    Cited by:

    1. Andrea Aveni & Ludovico Crippa & Giulio Principi, 2024. "On the Weighted Top-Difference Distance: Axioms, Aggregation, and Approximation," Papers 2403.15198, arXiv.org, revised Mar 2024.
    2. Andrea C. Hupman & Jay Simon, 2023. "The Legacy of Peter Fishburn: Foundational Work and Lasting Impact," Decision Analysis, INFORMS, vol. 20(1), pages 1-15, March.
    3. Yurun Ge & Lucas Bottcher & Tom Chou & Maria R. D'Orsogna, 2024. "A knapsack for collective decision-making," Papers 2409.13236, arXiv.org.

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