L’actualité sur l’Intelligence Collective

Catalogues d’articles scientifiques

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  • Shakespeare’s Theater of Judgment: Six Keywords By Kevin Curran, Edinburgh: Edinburgh University Press, 2024. 208 pp.
    by J Etxabe – International Journal of Law in Context, 2025 – cambridge.org on 2025-07-13 at 6:58 AM

    il y a 11 jours – … Curran identifies in Shakespeare’s theatre a form of collective intelligence – revealed not by anything that Shakespeare explicitly stated, the opinions he may have …

  • Individuation Through Integration: A PID-Based Study of Androids and Insect Societies
    by T Ikegami, S Baba, T Yoshida, H Kojima – ILIAD 2: ODYSSEY – openreview.net on 2025-07-13 at 6:58 AM

    il y a 10 jours – … environment that channels the behavior of individual modules or agents—bridging embodied AI and social insects under a unified framework of collective intelligence. …

  • SciArena: An Open Evaluation Platform for Foundation Models in Scientific Literature Tasks
    by Y Zhao, K Zhang, T Hu, S Wu, RL Bras… – arXiv preprint arXiv …, 2025 – arxiv.org on 2025-07-13 at 6:58 AM

    il y a 11 jours – … By leveraging collective intelligence, SciArena offers a community-driven evaluation of model performance on open-ended scientific tasks that demand literature-…

  • Pow-MUCB: A new client selection method based on Pow-d and modified UCB for federated learning in IoT
    by N Khajehali, J Yan, YW Chow, M Fahmideh – Internet of Things, 2025 – Elsevier on 2025-07-13 at 6:58 AM

    il y a 7 jours – Federated learning (FL) is a collaborative machine learning (ML) approach that enables distributed training between multiple clients, achieving collective intelligence …

  • Does Digital Collective Learning Improve with More Participants? An Experiment on a Collective Learning Platform
    by S Orejudo, O Casanova, J Cano Escoriaza… – Frontiers in … – frontiersin.org on 2025-07-13 at 6:58 AM

    il y a 8 jours – … Based on the principles of collective intelligence, our collaborative learning platform proposes an interaction model in which participants gradually reach solutions to a …

• SAGE Publications: Collective Intelligence: Table of Contents Table of Contents for Collective Intelligence. List of articles from both the latest and ahead of print issues.

  • A novel approach to studying the role influence plays in team collective intelligence
    by Lisa R O’Bryan, Timothy Oxendahl, Simon Garnier, Santiago Segarra, Matthew Wettergreen, Ashutosh Sabharwal, Margaret E Beier1Department of Psychological Sciences, 3990Rice University, Houston, TX, USA (LRO, TO, MEB)2Department of Electrical and Computer Engineering, 3990Rice University, Houston, TX, USA (LRO, SS, AS)3Department of Biological Sciences, 5965New Jersey Institute of Technology, Newark, NJ, USA (SG)4Department of Bioengineering, 3990Rice University, Houston, TX, USA (MW) on 2025-06-05 at 7:58 AM

    Collective Intelligence, Volume 4, Issue 2, April-June 2025. <br/>Studying collective intelligence in teams poses key challenges because individual contributions to team outcomes—such as ideas—can be abstract and difficult to measure. Although many studies have examined how team members shape collective outputs, they …

  • Life works through emergent collective intelligence: A conversation with Philip Ball
    by Philip Ball, Scott E Page1Independent Research (PB), USA2Ross School of Business, 33550University of Michigan-Ann Arbor, Ann Arbor, MI, USA (SEP) on 2025-05-21 at 2:08 AM

    Collective Intelligence, Volume 4, Issue 2, April-June 2025. <br/>

  • AI for collective intelligence
    by Christoph Riedl, David De CremerD’Amore-McKim School of Business, 1848Northeastern University, Boston, MA, USA on 2025-04-04 at 2:03 AM

    Collective Intelligence, Volume 4, Issue 2, April-June 2025. <br/>AI has emerged as a transformative force in society, reshaping economies, work, and everyday life. We argue that AI can not only improve short-term productivity but can also enhance a group’s collective intelligence. Specifically, AI can be employed to …

• OpenAlex

  • Collective Intelligence in Decision-Making with Non-Stationary Experts
    by RSS-Bridge on 2025-07-10 at 12:00 AM

    When sufficient experience to make informed decisions is unavailable, expert advice can help us navigate uncertainty. As expertise evolves, driven by continuous learning in human experts or model updates in artificial experts, it is crucial to adopt adaptive approaches. Existing methods for exploiting non-stationary experts focus on competing with the single best expert. In contrast, this work harnesses the power of collective intelligence to facilitate better decision-making in the face of evolving expertise or dynamic environments. To achieve this, we propose the novel CORVAL approach which optimally combines the insights of multiple experts. By adapting to drifts in expertise, our novel approach can surpass the performance of the single best expert as well as previous approaches. Empirical evaluations on a diverse range of non-stationary problems, including active learning applications, showcase the improved performance of our approach in collective decision-making scenarios.

  • Fostering collective intelligence in CPSS: an LLM-driven multi-agent cooperative tuning framework
    by RSS-Bridge on 2025-06-30 at 12:00 AM

    Cyber-Physical-Social Systems (CPSS) have emerged as a transformative paradigm in recent years, embracing computational processes, physical systems, and human social interactions within an integrated architectural framework. Advances in artificial intelligence technologies are targeted at addressing the complexity of CPSS design, especially in modeling human reactions in cyber-physical environment. Notably, LLM-based agents have shown significant potential, and numerous studies have leveraged multi-agent collaboration frameworks to solve reasoning tasks. Some approaches achieve multi-agent collaboration through a debate or communication setting. However, these approaches only use the existing capabilities of LLMs, fail to enhance their problem-solving performance. Other works incorporate the responses of other LLMs into their training trajectories to train individual LLMs in a reinforcement learning setting. We argue that effective collaboration should align not only in input information but also in consistent optimization objectives. Furthermore, in current cooperative frameworks, some LLMs tend to redundantly repeat others’ viewpoints, contributing minimally to solve problems. In this paper, inspired by multi-agent reinforcement learning research, we propose MACT, a Multi-Agent Cooperative Tuning framework to joint train multiple LLMs, ensuring that the optimization of each agent aligns directly with the objective of the global task. We equip each agent with a critic network to facilitate individual optimization. Furthermore, to encourage different agents to complement each other and contribute to the overall task, we employ a mixing network that ensures the value of each agent is monotonically consistent with the total value. Experimental results reveal that our method significantly enhances cooperative problem-solving capabilities in the LLM multi-agent framework, which set strong evidence for the modeling of human reaction within CPSS.

  • The epistemological consequences of large language models: rethinking collective intelligence and institutional knowledge
    by RSS-Bridge on 2025-06-28 at 12:00 AM

    Aucun résumé fourni pour cet article.

  • Exploring sustainable development scenarios using collective intelligence and system dynamics modelling: The lithium exploitation case
    by RSS-Bridge on 2025-06-19 at 12:00 AM

    Lithium-ion batteries play a pivotal role in renewable energy, powering a multitude of rechargeable devices. The transition to renewable sources amplifies the demand for lithium, providing advantages for nations with reserves but also posing potential risks to soil and water resources. Previous research has predominantly concentrated on distinct dimensions like social acceptance, water sustainability, and economic development. Limited attention has been given to an integrated analysis encompassing both the holistic and individual aspects simultaneously. We applied collective intelligence experiments and the system dynamics methodology to uncover the dynamic complexity of this issue and to reveal the relationship between variables and their interactions as they unfold over time. Our research suggests that, although there is some room for sustainable utilization, the risks for stakeholders exhibit clear asymmetry. While economic gains appear stable across various scenarios, the local community’s susceptibility becomes notably fragile due to excessive water exploitation.

  • Collective cooperative intelligence
    by RSS-Bridge on 2025-06-16 at 12:00 AM

    Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research.

Laboratoires

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Vidéos

• Fouloscopie

  • Les bousculades dans les stades de foot
    by Fouloscopie on 2025-06-13 at 9:37 PM

    Erratum: la pression se mesure effectivement en Newton par mètre carré. Désolé pour l’imprécision.Pour soutenir la chaîne :➡️ sur Tipeee : https://fr.tipeee.com/fouloscopie➡️ sur KissKiss : https://www.kisskissbankbank.com/fr/projects/fouloscopieMerci à mon invité Pascal Viot, pour en savoir plus sur ses activités :➡️ https://www.issue.ch/Pour en savoir plus sur mes recherches :➡️ Mon laboratoire de recherche à l’institut Max Planck : https://www.mpib-berlin.mpg.de/research/research-centers/adaptive-rationality➡️ Ma thèse de doctorat : http://mehdimoussaid.com/TheseMoussaid.pdfEnfin quelques références pour la vidéo :➡️ Pour l’étude des pressions physiquesWang, C., Shen, L., & Weng, W. (2020). Experimental study on individual risk in crowds based on exerted force and human perceptions. Ergonomics, 63(7), 789-803.➡️ Pour un rapport complet sur le drame de Hillsborough :Nicholson, C. E., & Roebuck, B. (1995). The investigation of the Hillsborough disaster by the Health and Safety Executive. Safety Science, 18(4), 249-259.➡️ Le blog de Keith Still donne des valeurs de références pour la pression: https://www.gkstill.com/CV/Modelling/Pressure.html➡️ Et le lien vers la vidéo du journal Le Monde : https://youtu.be/6_o8EwK-m7o

  • Combien faut-il de personnes pour lancer UNE RÉVOLUTION ? ✊✊✊
    by Fouloscopie on 2025-06-12 at 2:38 PM

    Grande nouvelle : je suis en train de préparer la deuxième saison des expériences participatives ! Inscrivez-vous ici pour être informé du lancement de la campagne, soutenir le projet et participer aux expériences : ➡️ https://www.kisskissbankbank.com/fr/projects/fouloscopie-100x-saison2 ⬅️Rendez-vous à la rentrée pour le démarrage du projet !Bonne vacances !

  • Peut-on travailler COMME DES FOURMIS ? 🐜 🐜 🐜
    by Fouloscopie on 2025-06-12 at 12:40 PM

    🔔 Pensez à vous abonner et à activer la cloche pour ne pas rater mes futures vidéos 🔔Pour soutenir la chaîne :➡️ sur Tipeee : https://fr.tipeee.com/fouloscopie➡️ sur KissKiss : https://www.kisskissbankbank.com/fr/projects/fouloscopiePour en savoir plus sur mes recherches :➡️ Mon laboratoire de recherche à l’institut Max Planck : https://www.mpib-berlin.mpg.de/research/research-centers/adaptive-rationality➡️ Ma thèse de doctorat : http://mehdimoussaid.com/TheseMoussaid.pdfEnfin quelques références bibliographiques :** Le livre complet d’Adam Smith, « La richesse des nations » est en accès libre ici : https://archive.org/details/in.ernet.dli.2015.206053/page/n21/mode/2up** Pour découvrir les travaux de Deborah Gordon, vous pouvez consulter ses publications comme celle-ci : https://web.stanford.edu/~dmgordon/old2/Gordon1996Organization.pdfOu écouter ses conférences fascinantes. Celle-ci est super par exemple: https://youtu.be/R07_JFfnFnY?si=UJXw3hlLUcjk8kxa** Le modèle de division du travail est formellement défini ici :Theraulaz, G., Bonabeau, E., & Denuebourg, J. N. (1998). Response threshold reinforcements and division of labour in insect societies. Proceedings of the Royal Society of London. Series B: Biological Sciences, 265(1393), 327-332.** Enfin voici la publication récente sur l’épreuve du porteur de piano : Dreyer, T., Haluts, A., Korman, A., Gov, N., Fonio, E., & Feinerman, O. (2025). Comparing cooperative geometric puzzle solving in ants versus humans. Proceedings of the National Academy of Sciences, 122(1), e2414274121.** Et un aperçu des résultats du jeu des biens communs avec ou sans sanction :Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public goods experiments. American Economic Review, 90(4), 980-994.

  • Peut-on s’organiser SANS CHEF ?
    by Fouloscopie on 2025-06-12 at 2:26 AM

    La foule peut-elle s’auto-organiser ? Comment apparaissent les leaders ? A-t-on toujours besoin d’un chef ?Pour répondre à ces questions, 100 abonnés de ma chaîne ont accepté de participer à une expérience sociale. Voici ce que l’on a découvert…*** POUR ME SOUTENIR ***➡️ sur Tipeee : https://fr.tipeee.com/fouloscopie➡️ sur KKBB : https://www.kisskissbankbank.com/fr/projects/fouloscopieMerci !*** MES RECHERCHES ***Mon laboratoire de recherche à l’institut Max Planck : https://www.mpib-berlin.mpg.de/research/research-centers/adaptive-rationalityMa thèse de doctorat : http://mehdimoussaid.com/TheseMoussaid.pdf** POUR ALLER PLUS LOIN **Le fameux “Sociobiology” D’Edward Wilson (je vous conseille la réédition): Wilson, E. O. (2000). Sociobiology: The new synthesis. Harvard University Press.Mon collègue Cédric Sueur partage régulièrement ses conférences en vidéo, je vous les recommande : https://sites.google.com/site/cedricsueuranimalbehaviour/videos?authuser=0

  • 100 personnes testent une méthode d’INTELLIGENCE COLLECTIVE
    by Fouloscopie on 2025-06-11 at 5:23 PM

    Le jugement de la foule est-il toujours fiable ? Comment influencer l’opinion collective ? Quels sont les mécanismes de l’intelligence collective ? Pour répondre à ces questions, 100 abonnés de ma chaîne ont accepté de participer à une expérience sociale. Voici ce que l’on a découvert…Après 7 jours, la vidéo avait atteint 247k vues : c’est donc réussi pour la quatrième prédiction ! *** POUR ME SOUTENIR ***➡️ sur Tipeee : https://fr.tipeee.com/fouloscopie➡️ sur KKBB : https://www.kisskissbankbank.com/fr/projects/fouloscopieMerci !*** MES RECHERCHES ***Mon laboratoire de recherche à l’institut Max Planck : https://www.mpib-berlin.mpg.de/research/research-centers/adaptive-rationalityMa thèse de doctorat : http://mehdimoussaid.com/TheseMoussaid.pdf** POUR ALLER PLUS LOIN **L’article original de Francis GaltonGalton, Francis. “Vox populi.” (1907).Plus d’information sur l’effet de l’influence collectiveLorenz, Jan, et al. “How social influence can undermine the wisdom of crowd effect.” Proceedings of the national academy of sciences 108.22 (2011): 9020-9025.Un de mes articles qui utilise la même méthode que sur la vidéoMoussaïd, Mehdi, et al. “Social influence and the collective dynamics of opinion formation.” PloS one 8.11 (2013): e78433.L’article fondateur sur la prédiction collectiveWolfers, Justin, and Eric Zitzewitz. “Prediction markets.” Journal of economic perspectives 18.2 (2004): 107-126.

Divers

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