What Should We Optimize in Participatory Budgeting? An Experimental Study

Ariel Rosenfeld, Nimrod Talmon

Research output: Working paper/PreprintPreprint

4 Downloads (Pure)

Abstract

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.
Original languageEnglish
StatePublished - 1 Nov 2021

Keywords

  • Computer Science - Multiagent Systems
  • Computer Science - Artificial Intelligence
  • Computer Science - Computer Science and Game Theory

Fingerprint

Dive into the research topics of 'What Should We Optimize in Participatory Budgeting? An Experimental Study'. Together they form a unique fingerprint.

Cite this