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

TL;DR

This paper introduces a machine learning-based method to learn and generate aggregation rules for participatory budgeting, improving decision-making by balancing social goals and adapting to various objectives.

Contribution

It presents a novel neural network approach that learns existing and creates new aggregation rules for participatory budgeting, enhancing flexibility and effectiveness.

Findings

Successfully generalizes from synthetic to real-world PB data

Learns existing aggregation rules and generates new, adaptable rules

Demonstrates improved decision-making in PB processes

Abstract

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.