# Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2307.12915 · HTML: https://mlsanity.com/paper/2307.12915/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Participatory Budgeting: Models and Approaches](https://mlsanity.com/paper/2003.00606/) (2020)
- [Participatory Budgeting: Data, Tools, and Analysis](https://mlsanity.com/paper/2305.11035/) (2023)
- [Pabulib: A Participatory Budgeting Library](https://mlsanity.com/paper/2012.06539/) (2020)
- [Improving City Life via Legitimate and Participatory Policy-making: A Data-driven Approach in Switzerland](https://mlsanity.com/paper/2306.13696/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies](https://mlsanity.com/paper/2406.11871/) (2024)

## Abstract (arXiv)

The legitimacy of bottom-up democratic processes for the distribution of public funds by policy-makers is challenging and complex. Participatory budgeting is such a process, where voting outcomes may not always be fair or inclusive. Deliberation for which project ideas to put for voting and choose for implementation lack systematization and do not scale. This paper addresses these grand challenges by introducing a novel and legitimate iterative consensus-based participatory budgeting process. Consensus is designed to be a result of decision support via an innovative multi-agent reinforcement learning approach. Voters are assisted to interact with each other to make viable compromises. Extensive experimental evaluation with real-world participatory budgeting data from Poland reveal striking findings: Consensus is reachable, efficient and robust. Compromise is required, which is though comparable to the one of existing voting aggregation methods that promote fairness and inclusion without though attaining consensus.