Author
Listed:
- Jason W. Burton
(Copenhagen Business School
Max Planck Institute for Human Development)
- Ezequiel Lopez-Lopez
(Max Planck Institute for Human Development)
- Shahar Hechtlinger
(Max Planck Institute for Human Development
Humboldt-Universität zu Berlin)
- Zoe Rahwan
(Max Planck Institute for Human Development)
- Samuel Aeschbach
(Max Planck Institute for Human Development
University of Basel)
- Michiel A. Bakker
(Google DeepMind)
- Joshua A. Becker
(University College London)
- Aleks Berditchevskaia
(Nesta)
- Julian Berger
(Max Planck Institute for Human Development
Humboldt-Universität zu Berlin)
- Levin Brinkmann
(Max Planck Institute for Human Development)
- Lucie Flek
(University of Bonn
Lamarr Institute for Machine Learning and Artificial Intelligence)
- Stefan M. Herzog
(Max Planck Institute for Human Development)
- Saffron Huang
(Collective Intelligence Project)
- Sayash Kapoor
(Princeton University
Princeton University)
- Arvind Narayanan
(Princeton University
Princeton University)
- Anne-Marie Nussberger
(Max Planck Institute for Human Development)
- Taha Yasseri
(University College Dublin
University College Dublin)
- Pietro Nickl
(Max Planck Institute for Human Development
Humboldt-Universität zu Berlin)
- Abdullah Almaatouq
(Massachusetts Institute of Technology)
- Ulrike Hahn
(University of London)
- Ralf H. J. M. Kurvers
(Max Planck Institute for Human Development
Technical University Berlin)
- Susan Leavy
(University College Dublin)
- Iyad Rahwan
(Max Planck Institute for Human Development)
- Divya Siddarth
(Collective Intelligence Project
Oxford University)
- Alice Siu
(Stanford University)
- Anita W. Woolley
(Carnegie Mellon University)
- Dirk U. Wulff
(Max Planck Institute for Human Development
University of Basel)
- Ralph Hertwig
(Max Planck Institute for Human Development)
Abstract
Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.
Suggested Citation
Jason W. Burton & Ezequiel Lopez-Lopez & Shahar Hechtlinger & Zoe Rahwan & Samuel Aeschbach & Michiel A. Bakker & Joshua A. Becker & Aleks Berditchevskaia & Julian Berger & Levin Brinkmann & Lucie Fle, 2024.
"How large language models can reshape collective intelligence,"
Nature Human Behaviour, Nature, vol. 8(9), pages 1643-1655, September.
Handle:
RePEc:nat:nathum:v:8:y:2024:i:9:d:10.1038_s41562-024-01959-9
DOI: 10.1038/s41562-024-01959-9
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