Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach

TL;DR

This paper introduces a multi-agent deep reinforcement learning method to improve fairness and decision-making in participatory budgeting, addressing choice overload and promoting equitable project selection.

Contribution

It presents a novel ethically aligned multi-agent reinforcement learning approach with a branching neural network architecture for scalable, fair decision support in participatory budgeting.

Findings

Fair compromises favor lower-cost projects

The approach enhances voter preference representation

Scalability achieved through novel neural network design

Abstract

Participatory budgeting is a method of collectively understanding and addressing spending priorities where citizens vote on how a budget is spent, it is regularly run to improve the fairness of the distribution of public funds. Participatory budgeting requires voters to make decisions on projects which can lead to ``choice overload". A multi-agent reinforcement learning approach to decision support can make decision making easier for voters by identifying voting strategies that increase the winning proportion of their vote. This novel approach can also support policymakers by highlighting aspects of election design that enable fair compromise on projects. This paper presents a novel, ethically aligned approach to decision support using multi-agent deep reinforcement learning modelling. This paper introduces a novel use of a branching neural network architecture to overcome scalability challenges of multi-agent reinforcement learning in a decentralized way. Fair compromises are found through optimising voter actions towards greater representation of voter preferences in the winning set. Experimental evaluation with real-world participatory budgeting data reveals a pattern in fair compromise: that it is achievable through projects with smaller cost.