参与式预算的公平信息流排序
Fair Feed Ranking for Participatory Budgeting
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中文总结 AI 辅助
针对参与式预算中提案排序的公平性问题，提出FairFeed排序设计，通过透明偏好和拒绝通道提升曝光公平性，模拟显示其优于现有排序。
中文摘要 AI 辅助
在大规模参与式预算中，公民无法检查全部提案池，因此提案的展示顺序成为一种议程设置权力。我们认为，公平曝光应被视为民主设计目标。我们研究了广泛部署的开源数字民主平台Consul Democracy，并表明其提案信息流通常按受欢迎程度、最新性或评论活动排序。基于这一诊断，我们提出了FairFeed，一种用于PB的信息流排序设计，它利用透明声明的偏好，提升曝光不足的提案，并允许限速的拒绝通道用于众包审查。我们在以慕尼黑2025年PB流程为基准的模拟中评估该设计，并与随机、最新和最多评论信息流进行比较。在该模拟中，FairFeed拓宽了提案发现，更均匀地分配了合格池中的可见性，增加了跨领域支持，并相对于基于评论的排序提高了对操纵的抵抗力。最后，我们概述了所需的人类受试者评估，以测试引导是否能足够准确地恢复选民偏好，以便在实践中部署。
英文摘要
In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power. We argue that fair exposure should therefore be treated as a democratic-design goal. We study Consul Democracy, a widely deployed open-source digital-democracy platform, and show that its proposal feeds are typically ordered by popularity, recency, or comment activity. Building on this diagnosis, we propose FairFeed, a feed-ranking design for PB that uses transparently declared preferences, boosts under-exposed proposals, and admits a rate-limited reject channel for crowd-sourced vetting. We evaluate the design in a simulation anchored in Munich's 2025 PB process and compare it with random, newest, and most-commented feeds. In this simulation, FairFeed broadens proposal discovery, distributes visibility more evenly across the eligible pool, increases cross-cutting support, and improves resistance to manipulation relative to comment-based ranking. We conclude by outlining the human-subjects evaluation needed to test whether onboarding can recover voter preferences accurately enough for deployment in practice.
发表机构
- University of Konstanz（康斯坦茨大学）
机构由 AI 辅助整理，请以论文原文为准。
