用多智能体强化学习帮市民投票更公平，发现小成本项目易达成共识。
Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach
- 用分支神经网络实现去中心化多智能体强化学习，解决可扩展性难题。
- 实验显示小成本项目更易促成公平妥协，提升选民偏好在结果中的体现率。
- 适合政策设计者和参与式预算平台，提升民主决策的公平性与效率。
参与式预算是一种公民共同决定公共资金分配方式，常用于提升财政分配的公平性。但选民需从多个项目中抉择，易产生选择过载。本文提出一种基于多智能体深度强化学习的决策支持方法，帮助选民制定提升其投票胜率的策略，同时为政策制定者提供选举设计优化建议。通过引入分叉神经网络架构，实现了去中心化下的高效多智能体学习，克服了传统方法的可扩展性瓶颈。实验基于真实参与式预算数据表明，公平妥协可通过低预算项目实现，且这些项目能更好反映选民偏好。该方法在提升代表性和降低决策负担方面展现出显著潜力。
原文摘要 · Abstract (English)
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.
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