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.

翻译：参与式预算（PB）为社区提供了一种通过投票分配公共资金给不同项目的民主流程。在实践中，PB组织者在选择聚合规则时面临挑战，原因在于他们要么不熟悉相关文献和现有规则的具体细节，要么没有现有规则符合其预期。本文提出了一种新颖的数据驱动方法，利用机器学习应对这一挑战。通过在PB实例上训练神经网络，我们的方法能够学习平衡社会福利、代表性及其他社会有益目标的聚合规则。该方法能够从小规模合成PB示例泛化到大规模真实世界PB实例，不仅能学习现有聚合规则，还能生成适应多样化目标的新规则，为PB流程提供更细致、以折衷为导向的解决方案。我们通过合成及真实世界PB数据的广泛实验验证了该方法的有效性，该方法有望拓展PB解决方案的应用与部署范围。