Large Language Models for Designing Participatory Budgeting Rules

TL;DR

This paper introduces LLMRule, a novel framework using large language models within an evolutionary search to automate the design of participatory budgeting rules, improving utility while maintaining fairness across diverse real-world instances.

Contribution

The paper presents a new framework that integrates LLMs into an evolutionary search for designing PB rules, addressing limitations of previous methods.

Findings

LLMRule outperforms existing handcrafted rules in utility.

LLM-generated rules maintain similar fairness levels.

Framework tested on over 600 real-world PB instances.

Abstract

Participatory budgeting (PB) is a democratic paradigm for deciding the funding of public projects given the residents' preferences, which has been adopted in numerous cities across the world. The main focus of PB is designing rules, functions that return feasible budget allocations for a set of projects subject to some budget constraint. Designing PB rules that optimize both utility and fairness objectives based on agent preferences had been challenging due to the extensive domain knowledge required and the proven trade-off between the two notions. Recently, large language models (LLMs) have been increasingly employed for automated algorithmic design. Given the resemblance of PB rules to algorithms for classical knapsack problems, in this paper, we introduce a novel framework, named LLMRule, that addresses the limitations of existing works by incorporating LLMs into an evolutionary search procedure for automating the design of PB rules. Our experimental results, evaluated on more than 600 real-world PB instances obtained from the U.S., Canada, Poland, and the Netherlands with different representations of agent preferences, demonstrate that the LLM-generated rules generally outperform existing handcrafted rules in terms of overall utility while still maintaining a similar degree of fairness.