用大模型自动设计更公平高效的公共预算分配规则
Large Language Models for Designing Participatory Budgeting Rules
- 将大模型嵌入进化搜索，自动生成预算分配规则
- 在600多个真实案例中，整体效益优于人工规则
- 适合政策设计、算法民主化研究者参考
参与式预算（Participatory Budgeting, PB）是一种基于居民偏好决定公共项目资金分配的民主模式，已在世界多地城市应用。其核心在于设计规则，即在预算约束下返回可行分配方案的函数。由于需兼顾效用与公平性，且需深厚领域知识，传统规则设计困难重重。近期大语言模型（LLMs）被用于自动化算法设计。鉴于PB规则与经典背包问题算法的相似性，本文提出新框架LLMRule，将LLMs融入进化搜索过程，实现PB规则的自动化设计。实验在来自美国、加拿大、波兰和荷兰的600多个真实PB实例上进行，涵盖不同代理人偏好表示方式。结果表明，LLM生成的规则在整体效用上普遍优于现有手工规则，同时保持相近的公平水平。
原文摘要 · Abstract (English)
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.
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