ML Anthology

Preference Elicitation for Participatory Budgeting

Gerdus Benade, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah
AAAI 2017 pp. 376-382
doi:10.1609/AAAI.V31I1.10563 /aaai/2017/benade2017aaai-preference/

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 a rethinking of some of the basics of computational social choice, including the very way in which individuals express their preferences. We analytically compare four preference elicitation methods -- knapsack votes, rankings by value or value for money, and threshold approval votes -- through the lens of implicit utilitarian voting, and find that threshold approval votes are qualitatively superior. This conclusion is supported by experiments using data from real participatory budgeting elections.

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Text

Benade et al. "Preference Elicitation for Participatory Budgeting." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10563

Markdown

[Benade et al. "Preference Elicitation for Participatory Budgeting." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/benade2017aaai-preference/) doi:10.1609/AAAI.V31I1.10563

BibTeX

@inproceedings{benade2017aaai-preference,
  title     = {{Preference Elicitation for Participatory Budgeting}},
  author    = {Benade, Gerdus and Nath, Swaprava and Procaccia, Ariel D. and Shah, Nisarg},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {376-382},
  doi       = {10.1609/AAAI.V31I1.10563},
  url       = {https://mlanthology.org/aaai/2017/benade2017aaai-preference/}
}