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Scaffolding cooperation in human groups with deep reinforcement learning

Computer Science

Scaffolding cooperation in human groups with deep reinforcement learning

K. R. Mckee, A. Tacchetti, et al.

This groundbreaking research conducted by Kevin R. McKee, Andrea Tacchetti, Michiel A. Bakker, Jan Balaguer, Lucy Campbell-Gillingham, Richard Everett, and Matthew Botvinick uses deep reinforcement learning to boost cooperation in human groups, achieving a whopping 77.7% cooperation rate. Discover how a 'social planner' AI can transform cooperation dynamics in network games!

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Playback language: English
Abstract
This paper explores the use of deep reinforcement learning to enhance cooperation in human groups. A 'social planner' AI agent was trained using simulation to make recommendations for connecting or disconnecting individuals within a network cooperation game. The trained agent significantly improved cooperation rates in groups of human participants playing for real monetary stakes, achieving a 77.7% cooperation rate compared to 42.8% in control groups. Unlike previous methods that separate defectors, the social planner employed a conciliatory approach, guiding defectors towards smaller, cooperative groups.
Publisher
Nature Human Behaviour
Published On
Sep 07, 2023
Authors
Kevin R. McKee, Andrea Tacchetti, Michiel A. Bakker, Jan Balaguer, Lucy Campbell-Gillingham, Richard Everett, Matthew Botvinick
Tags
deep reinforcement learning
cooperation
AI agent
network game
human groups
conciliatory approach

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