A Statistical Framework for Algorithmic Collective Action with Multiple Collectives

2026-05-07

AI Summary

This work addresses a critical gap in algorithmic collective action research, which has largely focused on single collectives and thus fails to capture how multiple, decentralized groups—each pursuing related yet distinct objectives—jointly influence shared learning systems. The paper proposes the first statistical theoretical framework for multi-collective algorithmic action, specifically examining how distinct collectives coordinate data modifications to collectively steer model behavior in classification tasks. Under partial observability of other collectives’ strategies, the authors integrate statistical learning theory, game-theoretic analysis, and probabilistic bounds to derive a computable success criterion. This bound explicitly quantifies the interplay between collective size and alignment of objectives, and its predictive accuracy for multi-collective coordination efficacy is validated through simulations of climate adaptation interventions in smart cities.

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.

Problem

• Algorithmic Collective Action
• Multiple Collectives
• Statistical Framework
• Collective Coordination
• Classification

Innovation

• Algorithmic Collective Action
• multiple collectives
• statistical framework
• classifier influence
• collective coordination