Scaffolding cooperation in human groups with deep reinforcement learning

Kevin R. McKee (), Andrea Tacchetti, Michiel A. Bakker, Jan Balaguer, Lucy Campbell-Gillingham, Richard Everett and Matthew Botvinick
Additional contact information
Kevin R. McKee: Google DeepMind
Andrea Tacchetti: Google DeepMind
Michiel A. Bakker: Google DeepMind
Jan Balaguer: Google DeepMind
Lucy Campbell-Gillingham: Google DeepMind
Richard Everett: Google DeepMind
Matthew Botvinick: Google DeepMind

Nature Human Behaviour, 2023, vol. 7, issue 10, 1787-1796

Abstract: Abstract Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and simulation methods to train a ‘social planner’ capable of making recommendations to create or break connections between group members. The strategy that it develops succeeds at encouraging pro-sociality in networks of human participants (N = 208 participants in 13 groups) playing for real monetary stakes. Under the social planner, groups finished the game with an average cooperation rate of 77.7%, compared with 42.8% in static networks (N = 176 in 11 groups). In contrast to prior strategies that separate defectors from cooperators (tested here with N = 384 in 24 groups), the social planner learns to take a conciliatory approach to defectors, encouraging them to act pro-socially by moving them to small highly cooperative neighbourhoods.

Date: 2023
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DOI: 10.1038/s41562-023-01686-7

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