How large language models can reshape collective intelligence

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 Flek, Stefan M. Herzog, Saffron Huang, Sayash Kapoor, Arvind Narayanan, Anne-Marie Nussberger, Taha Yasseri, Pietro Nickl, Abdullah Almaatouq, Ulrike Hahn, Ralf H. J. M. Kurvers, Susan Leavy, Iyad Rahwan, Divya Siddarth, Alice Siu, Anita W. Woolley, Dirk U. Wulff and Ralph Hertwig
Additional contact information
Jason W. Burton: Copenhagen Business School
Ezequiel Lopez-Lopez: Max Planck Institute for Human Development
Shahar Hechtlinger: Max Planck Institute for Human Development
Zoe Rahwan: Max Planck Institute for Human Development
Samuel Aeschbach: Max Planck Institute for Human Development
Michiel A. Bakker: Google DeepMind
Joshua A. Becker: University College London
Aleks Berditchevskaia: Nesta
Julian Berger: Max Planck Institute for Human Development
Levin Brinkmann: Max Planck Institute for Human Development
Lucie Flek: University of Bonn
Stefan M. Herzog: Max Planck Institute for Human Development
Saffron Huang: Collective Intelligence Project
Sayash Kapoor: Princeton University
Arvind Narayanan: Princeton University
Anne-Marie Nussberger: Max Planck Institute for Human Development
Taha Yasseri: University College Dublin
Pietro Nickl: Max Planck Institute for Human Development
Abdullah Almaatouq: Massachusetts Institute of Technology
Ulrike Hahn: University of London
Ralf H. J. M. Kurvers: Max Planck Institute for Human Development
Susan Leavy: University College Dublin
Iyad Rahwan: Max Planck Institute for Human Development
Divya Siddarth: Collective Intelligence Project
Alice Siu: Stanford University
Anita W. Woolley: Carnegie Mellon University
Dirk U. Wulff: Max Planck Institute for Human Development
Ralph Hertwig: Max Planck Institute for Human Development

Nature Human Behaviour, 2024, vol. 8, issue 9, 1643-1655

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41562-024-01959-9

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