Participatory Budgeting (PB) is commonly studied from an axiomatic perspective, where the aim is to design procedurally fair and economically efficient rules for voters with full information regarding their preferences. In contrast, we take an epistemic perspective and consider a framework where PB projects have different levels of underlying quality, indicating how well the project will take effect, which cannot be directly observed before implementation. Agents with noisy information cast votes to aggregate their information, and aim to elect a high-quality set of projects. We evaluate the performance of common PB rules by measuring the expected utility of their outcomes, compared to the optimal set of projects. We find that the quality of approximation improves as the range of project costs shrinks. When projects have unit cost, these common rules can identify the ``best'' set with probability converging to 1. We also study whether strategic agents have incentives to honestly convey their information in the vote. We find that it happens only under very restrictive conditions. We also run numerical experiments to examine the performance of different rules empirically and support our theoretical findings.

翻译：参与式预算通常从公理化视角进行研究，其目标是为具有完整偏好信息的投票者设计程序公平且经济高效的规则。与此相反，我们采用认知视角，考虑一个框架：参与式预算项目具有不同的潜在质量水平（反映项目实施后的预期效果），而这些质量在实施前无法直接观测。拥有噪声信息的智能体通过投票聚合信息，旨在选出高质量的项目组合。我们通过比较常见参与式预算规则产出结果的期望效用与最优项目组合的效用，评估这些规则的性能。研究发现，当项目成本范围缩小时，规则对最优解的近似质量会提升。当项目具有单位成本时，这些常见规则能以趋近于1的概率识别出"最优"项目组合。我们还研究了策略性智能体是否具有在投票中诚实传递信息的动机，发现这仅在极其严格的条件下才会发生。我们进一步通过数值实验实证检验不同规则的性能，以支持理论研究发现。