The tragedy of the cognitive commons: collective intelligence beyond AI-induced knowledge collapse

• URL: http://arxiv.org/abs/2607.13272v1
• Date: Tue, 14 Jul 2026 21:13:58 GMT
• Title: The tragedy of the cognitive commons: collective intelligence beyond AI-induced knowledge collapse
• Abstract summary: Acemoglu, Kong and Ozdaglar (2026a) agentic AI can cause a self-reinforcing deterioration of humanity's common knowledge base.<n>This paper offers a measured appraisal of the model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a recent dynamic model by Acemoglu, Kong and Ozdaglar (2026a) agentic AI can cause a self-reinforcing deterioration of humanity's common knowledge base in what they call knowledge collapse. The model is based on a natural complementarity between the cumulative general knowledge of humans and locally generated context-specific knowledge, and on a learning externality that means that we all contribute to the private signal and the thin public signal that feeds the collective stock. If agentic AI can substitute for the private signal but not rebuild the public signal, and when human effort is sufficiently elastic, we can reach a low-knowledge equilibrium. This paper offers a measured appraisal of the model. It assesses the model in terms of what the popular summaries say is common knowledge, the world literature against knowledge commons and model collapse, and the partial empirical evidence, such as a 25% decline in public knowledge sharing on Stack Overflow. It then presents five structural criticisms of the model: its fixed knowledge taxonomy, the assumption that AI cannot provide general knowledge, the unmeasured effort elasticity parameter, and the political futility of the model to be implemented. We argue that the model's value lies not in any guaranteed catastrophe but in identifying a credible negative externality, the tragedy of the cognitive commons, whose remedy is a better aggregation of human knowledge. The paper outlines a research agenda for the study of effort elasticity and knowledge commons.

Related papers

• Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution [80.98492754957466]
  Core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously.<n>This paper argues that causality is necessary to understand and balance trade-offs in performance and multiple objectives of trustworthy AI.
  arXiv  Detail & Related papers  (2026-05-04T14:26:28Z)
• Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective [18.284119243990062]
  Implicit knowledge is unexternalized because documentation cost exceeds perceived value.<n>Current reliability methods can only verify explicit knowledge against sources.<n>We propose Knowledge Objects (KOs) -- structured artifacts that externalize implicit knowledge into forms humans can inspect, verify, and endorse.
  arXiv  Detail & Related papers  (2026-05-03T18:31:45Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Information Retrieval Induced Safety Degradation in AI Agents [52.15553901577888]
  This study investigates how expanding retrieval access affects model reliability, bias propagation, and harmful content generation.<n>Retrieval-enabled agents built on aligned LLMs often behave more unsafely than uncensored models without retrieval.<n>These findings underscore the need for robust mitigation strategies to ensure fairness and reliability in retrieval-enabled and increasingly autonomous AI systems.
  arXiv  Detail & Related papers  (2025-05-20T11:21:40Z)
• AI and the Problem of Knowledge Collapse [0.0]
  We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. We provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount.
  arXiv  Detail & Related papers  (2024-04-04T15:06:23Z)
• Position: Stop Making Unscientific AGI Performance Claims [6.343515088115924]
  Developments in the field of Artificial Intelligence (AI) have created a 'perfect storm' for observing'sparks' of Artificial General Intelligence (AGI) We argue and empirically demonstrate that the finding of meaningful patterns in latent spaces of models cannot be seen as evidence in favor of AGI. We conclude that both the methodological setup and common public image of AI are ideal for the misinterpretation that correlations between model representations and some variables of interest are 'caused' by the model's understanding of underlying 'ground truth' relationships.
  arXiv  Detail & Related papers  (2024-02-06T12:42:21Z)
• Visually Grounded Commonsense Knowledge Acquisition [132.42003872906062]
  Large-scale commonsense knowledge bases empower a broad range of AI applications. Visual perception contains rich commonsense knowledge about real-world entities. We present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem.
  arXiv  Detail & Related papers  (2022-11-22T07:00:16Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• ArT: All-round Thinker for Unsupervised Commonsense Question-Answering [54.068032948300655]
  We propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating. We evaluate it on three commonsense QA benchmarks: COPA, SocialIQA and SCT.
  arXiv  Detail & Related papers  (2021-12-26T18:06:44Z)
• DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge [42.08569149041291]
  We propose an alternative commonsense knowledge acquisition framework DISCOS. DISCOS populates expensive commonsense knowledge to more affordable linguistic knowledge resources. We can acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC on the core part of ASER.
  arXiv  Detail & Related papers  (2021-01-01T03:30:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.