用机器学习自动学出更公平的公共预算分配规则
Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach
- 用神经网络从投票数据中学习预算分配规则
- 在真实和模拟数据上都表现良好，能平衡公平与效率
- 适合想优化公共决策流程的研究者与实践者
参与式预算（PB）为社区通过投票决定公共资金分配提供民主机制。现实中，组织者常因不熟悉现有规则或找不到符合预期的规则而难以选择。本文提出一种新型数据驱动方法，利用机器学习在PB实例上训练神经网络，学习既能提升社会福利又能保障代表性的分配规则。该方法可从小规模合成数据泛化到大规模真实场景，不仅能复现已有规则，还能生成适应不同目标的新规则，提供更精细、折衷的解决方案。通过大量合成与真实数据实验验证了其有效性，有助于推动PB技术的应用与落地。
原文摘要 · Abstract (English)
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.
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