多群体协同影响模型行为，给出可计算的成功边界。
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
- 构建首个支持多群体协同的统计框架，分析多方如何影响分类器。
- 量化成功概率，揭示群体规模与目标一致性的关键作用。
- 仅需部分信息即可计算，适用于城市气候适应等真实场景。
随着学习系统日益影响日常决策，算法集体行动（ACA）——即用户协同修改共享数据以引导模型行为——成为监管政策和企业模型设计的重要补充。现实中，集体行动常分散为多个具有共同目标但规模、策略和行动目标各异的群体。然而，现有ACA研究多聚焦单一集体。为此，本文提出首个针对多集体协同的综合性统计框架。重点研究分类任务中多集体如何影响分类器行为，提供关于集体行动成功率的定量统计边界，考虑各集体规模及其目标对齐程度的相互作用。通过仅需部分其他集体信息，使每方都能计算这些边界。最后，基于智能城市气候适应干预的模拟实验，验证了该框架的有效性。
原文摘要 · Abstract (English)
As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to steer model behavior, offers a complement to regulator-side policy and corporate model design. Real-world collective actions have traditionally been decentralized and fragmented into multiple collectives, despite sharing overarching objectives, with each collective differing in size, strategy, and actionable goals. However, most of the ACA literature focuses on single collective settings. To address this, we propose the first comprehensive statistical framework for ACA with multiple collectives acting on the same system. In particular, we focus on collective action in classification, studying how multiple collectives can influence a classifier's behavior. We provide quantitative statistical bounds on the success of the collectives, considering the role and the interplay of the collectives' sizes and the alignment of their goals. We make such bounds computable by each collective with only partial knowledge of other collectives' sizes and strategies. Finally, we numerically illustrate our framework on simulations inspired by interventions for climate adaptation in smart cities, demonstrating the usefulness of our bounds.
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