Title: Sync or Sink: Bounds on Algorithmic Collective Action with Noise and Multiple Groups
Venue: NeurIPS 2025
Accepted: no
Keywords: Algorithmic Collective Action

Social Computing

Data Campaigns
Abstract: Collective action against algorithmic systems, which enables groups to promote their own interests, is poised to grow. Hence, there will be growth in the size and the number of distinct collectives. Currently, there is no formal analysis of how coordination challenges within a collective can impact downstream outcomes, or how multiple collectives may affect each other's success. In this work, we aim to provide guarantees on the success of collective action in the presence of both coordination noise and multiple groups. Our insight is that data generated by either multiple collectives or by coordination noise can be viewed as originating from multiple data distributions. 
Using this framing, we derive bounds on the success of collective action. We conduct experiments to study the effects of noise on collective action. We find that sufficiently high levels of noise can reduce the success of collective action. In certain scenarios, large noise can sink a collective success rate from $100$% to just under $60$%. We identify potential trade-offs between collective size and coordination noise; for example, a collective that is twice as big but with four times more noise experiencing worse outcomes than the smaller, more coordinated one. This work highlights the importance of understanding nuanced dynamics of strategic behavior in algorithmic systems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn collective action, a group of users strategically modifies their own data to inject a correlation between the transformation and a target label in learned classifier. This paper builds on top of previous works and provides a theoretical framework for a multi-collective setting, by combining the theoretical framework for a single collective with empirical results for multiple collectives.\nThe paper focuses on theoretical lower bounds for the success of a collective in a two-collectives setting, but provides a generalized bound for n collective as well.\nThey then use this framework to model noisy strategies, simulating users who fail to follow the coordinated strategy perfectly, and then empirically test the effect of different types of noises on the success.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper provides a generalization of the lower bound on a collective success in a multi-collective settings, which natural and valuable extension of prior work.\n2. The writing is clear, and the structure of the paper, including the background and problem formulation, makes the subject accessible.\n3. Figures are well designed and effectively convey the message of the research questions.\n\nWeaknesses:\n1. While the generalized success bound is important to formalize, its derivation is very similar to the single collective analysis and does not yield surprising insight or fundamental understanding.\n2. The experimental settings are limited. The paper reuses the same setup from prior work without exploring new scenarios that may be more fitting for a multi-collective setting. For example, the instruction \"Place character 'A' every 20 words\" is simple and unlikely to be misunderstood. More ambiguous or realistic problems would be more appropriate to test the theory.\n3. The concept of the label noise experiments is unclear. If the goal is to create a correlation between a signal $g$ and a target label $ y^* $, then it is unclear why $ y^* $ would be noisy. If the members of the collective misunderstood the the goal itself, it is unlikely they would have joined the collective in the first place. It would help if the authors justified where label noise arises, or find more justified settings, especially since the key experimental results (Fig. 3A) depend on it. In the current context, a more suiting interpretation is a multi-collective problem where every collective has a similar $g$ but different $y^*$, which is a different problem with a different meaning of the result.\n4. Several details of the experiment design are not fully explained (see questions section below).\n\nI would like to accept the paper, as it is valuable to have this generalization formally documented. However, in the current form I believe the paper does not contribute enough beyond prior work to warrant acceptance. The theory is sound, but relatively straightforward. To help me raise the score to accept, I think the paper should offer new experimental settings (especially ones which are more plausible for inter-collective miscommunication), and a clearer justification for the noise sources, particularly label noise.\n\n\nPresentation comments (do not affect my scoring):\n1. Figure fonts are very small. Please enlarge for readability.\n2. Several paragraphs omit the line numbering (e.g. 113-114, 138-139...). You should look into that.\n3. Inline math is often followed by a missing punctuation or spacing (e.g. lines 115, 198...).\n4. Line 211 refers to figure axes before naming the actual figure. Rephrasing will improve quality.\n\nQUESTIONS:\n1. As mentioned above, the motivation for label noise is unclear. What is the rationale for introducing label noise?\n2. In the experiment with the the random characters subset, why were these specific characters chosen? Is the choice of characters meaningful, or only the size of the subset? Did you investigate how varying the subset size affect the success?\n3. What exactly is randomized in each experiment run? Are the collectives randomized per run?\n4. The standard deviations in the figures are quite large. Is there an intuitive explanation for this variability? What are the causes for the variability?\n5. In section 7.1, you mention an example where two collectives with different $g$ and $ y^* $ that still hinder each other's success. This is counterintuitive. What is the context and how do the collectives interfere each other? You explain that it is because of *character modifications that look sufficiently “close”*. What does *\"close\"* mean in this context and how does it lead to interference?\n6. This paper models the population and noise sources as distributions. I'm not sure if that's the best modeling choice. Would a finite-sample approach, such as in [1] be more suitable to discuss miscommunication?\n\n[1] Gauthier, Etienne, Francis Bach, and Michael I. Jordan. \"Statistical Collusion by Collectives on Learning Platforms.\" arXiv preprint arXiv:2502.04879 (2025).\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper contributes to the line of work on algorithmic collective action, by moving away from perfect coordination. In the algorithmic collective action problem a group of \"manipulators\", each holding a single data point, want to fool a classifier. The paper offers both theoretical upper bounds and experiments.\n\nSTRENGTHS AND WEAKNESSES:\nI found the formal model and the presentation quite confusing. While I get the intuition that the authors are trying to capture, it is not clear to me why it is captured by the formal model.\n\nFirst, the strategy being a mapping Z\\to Z is a non-obvious choice. In particular, it presumes that the strategy is anonymous, i.e., all manipulators act in the same way given the same inputs. This is not without loss of generality, but probably appropriate for the settings studied in this work. Still, worth a comment.\n\nMore importantly, I do not understand the point of a signal and how it is different from a strategy. Given the motivating example, I expected the manipulator's goal to get the classifier to output a specific label on a certain subset of inputs, but this seems different? The definition of success is then in terms of the signal, not in terms of the strategy. What is the role of strategy then?\n\nThe paper provides some lower bounds on success rates; these are complicated expressions, so I am not sure how useful they are. Also, the paper does not say if they are tight. Also, the paper does not explicitly analyze the difference between these bounds and the bounds for the coordinated case from prior work. Overall, I found it very hard to evaluate the contribution, because the model is not well-explained; also the paper does not seem to be proofread properly.\n\nA few minor issues: \n\n71: success->success of\n\nIn the unnumbered chunk between 113 and 114, \"Where\" should not be capitalized\n\nalso, 3 lines below 113 there is a missing whitespace\n\n128: something is missing here\n\n130 one's->1's\n\n133 is -> be\n\n136 missing full stop\n\nDisplay formula below 138: what is \\mathcal X^*?\n\n176: the explanation of what r is is confusing\n\n183: how do you implement \"arbitrary location\"?\n\n198: missing whitespace\n\nTable 1, last line: remove one of \"place\" and \"insert\"\n\n202-203: why are the ranges so different for the two experiments?\n\n220-221: this sentence does not parse\n\nTheorem 1: epsilon_1 -> epsilon_1-suboptimal\n\nQUESTIONS:\nCan you elaborate on the role of signal and strategy in your model?\n\nLIMITATIONS:\naddressed in the paper", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper provides guarantees on the success of collective action in the presence of both coordination noise and multiple groups. One of the major insights is that data generated by either multiple collectives or by coordination noise can be viewed as originating from multiple data distributions.

STRENGTHS AND WEAKNESSES:
**Weaknesses:** The review's primary concern is the paper's positioning. The reviewer is not entirely certain that the work, as presented, falls within the scope of the machine learning field. The link could be made much stronger.

QUESTIONS:
- Could the authors please provide some concrete examples of "algorithmic collective action" within the context of machine learning?
- Could the authors elaborate further on the implications of your main results for machine learning?
- What does "Sync or Sink" in the title refer to? Could the authors clarify its meaning?

LIMITATIONS:
A potential limitation, which could also be framed as a limitation, is the paper's unclear connection to the machine learning domain. The authors should consider explicitly discussing the boundaries and the scope of their findings in relation to typical machine learning problems.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the effectiveness of algorithmic collective action in the presence of coordinated noise and multiple collectives, proposes a theoretical lower bound and conducts empirical analysis. It is found that data perturbations, whether caused by multiple collectives or uncoordinated behaviors within a collective, can be modeled as multiple data distributions, and for the first time, a lower bound on the success rate of collective action is proposed based on this perspective. The impact of noise is studied through resume classification experiments, and it is found that high noise can significantly reduce the success rate, and small-scale low-noise groups may be better than large-scale high-noise groups.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: This paper unifies noise and multiple groups into a multi-distribution problem for the first time and establishes a rigorous mathematical framework. It clearly demonstrates the trade-off between organizational coordination and collective scale, and through experiments with specific noise types and strategies, reveals the differential impact of noise on different strategies. In addition, this research has clear practical value and can provide a reference for decision-making on whether to prioritize coordination of small groups or expansion of scale in the future.\n\nWeaknesses: If the distribution caused by group behavior or noise is difficult to clearly divide in practice, it may make the distribution difficult to separate. In addition, it mainly focuses on text classification tasks, the data scale and type are relatively simple, and the experiment is only conducted on a multi-label classification set. In addition, only benign noise is considered. If it is completely interfering noise, it may have an impact on the model. Finally, the differences in the sensitivity of different learning algorithms to noise are not considered.\n\nQUESTIONS:\nMy questions are:\nQ1: Have the authors condered using more detailed explanations or vivid demonstrations of the problem being solved in the main part of the paper?\nQ2. In the experiment, the target categories only involved high and low frequencies, and did not cover the characteristics of medium frequencies or more detailed categories. Does this paper need this consideration?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}