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Collective Intelligence as Collective Information Processing

Author

Listed:
  • Anwarzai, Zara
  • Moser, Cody James
  • Dromiack, Hannah
  • Garg, Ketika

    (Caltech)

  • Ramos-Fernandez, Gabriel

Abstract

Collective intelligence research spans multiple disciplines and focuses on a broad range of collective behaviors, including group problem-solving, flocking in social animals, and the formation of social knowledge. It is not apparent what these different forms of collective intelligence have in common, apart from being instances of collective behavior. In this paper, we develop a framework that enables us to better classify different forms of collectively intelligent behavior in relation to one another based on the information processing mechanisms involved. We argue that these behaviors share a common foundation, which we call collective information processing, or CIP. CIP involves two key mechanisms: (1) individual processing of group information and (2) group processing, or group-level sensitivity to the arrangement of individual information. We operationalize the CIP framework to analyze different forms of collective intelligence, both classifying them in relation to one another and in alignment with generalized quantifiable measures of information processing. Our account of collective intelligence as CIP offers a novel framework for identifying and classifying forms of collective intelligence across a wide range of disciplinary contexts. This framework is meant to unify and subsume, rather than simply challenge, existing attempts to define collective intelligence.

Suggested Citation

  • Anwarzai, Zara & Moser, Cody James & Dromiack, Hannah & Garg, Ketika & Ramos-Fernandez, Gabriel, 2025. "Collective Intelligence as Collective Information Processing," SocArXiv kg8xm_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:kg8xm_v1
    DOI: 10.31219/osf.io/kg8xm_v1
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    1. Tarana Nigam & Caspar M. Schwiedrzik, 2024. "Predictions enable top-down pattern separation in the macaque face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Timothy Feddersen & Wolfgang Pesendorfer, 1997. "Voting Behavior and Information Aggregation in Elections with Private Information," Econometrica, Econometric Society, vol. 65(5), pages 1029-1058, September.
    3. Pavel Atanasov & Phillip Rescober & Eric Stone & Samuel A. Swift & Emile Servan-Schreiber & Philip Tetlock & Lyle Ungar & Barbara Mellers, 2017. "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls," Management Science, INFORMS, vol. 63(3), pages 691-706, March.
    4. Joaquin Navajas & Tamara Niella & Gerry Garbulsky & Bahador Bahrami & Mariano Sigman, 2018. "Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds," Nature Human Behaviour, Nature, vol. 2(2), pages 126-132, February.
    5. Daniel Barkoczi & Mirta Galesic, 2016. "Social learning strategies modify the effect of network structure on group performance," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    6. Zhi Da & Xing Huang, 2020. "Harnessing the Wisdom of Crowds," Management Science, INFORMS, vol. 66(5), pages 1847-1867, May.
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