# Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach

arXiv: https://arxiv.org/abs/2412.01864 · HTML: https://mlsanity.com/paper/2412.01864/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 1 Dec 2024.

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

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [The (Computational) Social Choice Take on Indivisible Participatory Budgeting](https://mlsanity.com/paper/2303.00621/) (2023)
- [Participatory Budgeting with Cumulative Votes](https://mlsanity.com/paper/2009.02690/) (2020)
- [Pabulib: A Participatory Budgeting Library](https://mlsanity.com/paper/2012.06539/) (2020)
- [Ties in Multiwinner Approval Voting](https://mlsanity.com/paper/2305.01769/) (2023)
- [Proportional Participatory Budgeting with Projects Interaction](https://mlsanity.com/paper/2106.05360/) (2021)

## Built on by (most cited first, 2 of 2)

- [What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting](https://mlsanity.com/paper/2508.06454/) (2025)
- [Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment](https://mlsanity.com/paper/2602.03003/) (2026)

## Abstract (arXiv)

Participatory Budgeting (PB) offers a democratic process for communities to allocate public funds across various projects through voting. In practice, PB organizers face challenges in selecting aggregation rules either because they are not familiar with the literature and the exact details of every existing rule or because no existing rule echoes their expectations. This paper presents a novel data-driven approach utilizing machine learning to address this challenge. By training neural networks on PB instances, our approach learns aggregation rules that balance social welfare, representation, and other societal beneficial goals. It is able to generalize from small-scale synthetic PB examples to large, real-world PB instances. It is able to learn existing aggregation rules but also generate new rules that adapt to diverse objectives, providing a more nuanced, compromise-driven solution for PB processes. The effectiveness of our approach is demonstrated through extensive experiments with synthetic and real-world PB data, and can expand the use and deployment of PB solutions.