arXiv cs.AI· Claudio Battiloro, Pietro Greiner, Dario Rancati, Bret Nestor, Oumaima Amezgar, Francesca Dominici·· 4 小时前AI 评分27
面向多个集体的算法集体行动统计框架研究
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
AI 导读
一篇 NeurIPS 2026 论文提出首个针对多个集体作用于同一系统的算法集体行动（ACA）综合统计框架，研究多集体如何影响分类器的行为。论文给出了集体行动成功与否的定量统计边界，考虑各集体规模及其目标一致性的作用，且各集体只需掌握其他集体规模与策略的部分知识即可计算这些边界。作者还以智慧城市气候适应干预为灵感的模拟实验验证了边界的有效性。
正文 · 原文
Abstract: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.
| Comments: | NeurIPS 2026. 35 pages, 17 figures, 4 tables |
| Subjects: | Methodology (stat.ME); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.06749 [stat.ME] |
| (or arXiv:2605.06749v3 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06749 arXiv-issued DOI via DataCite |
Submission history
From: Claudio Battiloro Dr [view email]
[v1]
Thu, 7 May 2026 15:45:53 UTC (5,094 KB)
[v2]
Mon, 9 Nov 2026 18:42:38 UTC (5,710 KB)
[v3]
Tue, 10 Nov 2026 14:59:01 UTC (5,710 KB)
来源：arXiv cs.AI · arxiv.org