A multi-agent reinforcement learning model of reputation and cooperation in human groups
This addresses the challenge of understanding when and where cooperation occurs in social dilemmas for researchers in psychology and AI, though it is incremental as it builds on existing experimental and computational work.
The study tackled the problem of how social-cognitive mechanisms like reputation influence the spatial and temporal coordination of collective action in human groups, using a multi-agent reinforcement learning model in the Clean Up task. The result showed that human groups cooperate effectively with identifiable reputations but fail under anonymity, and the model accurately predicted non-territorial, turn-taking strategies.
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the first part of this process, demonstrating that a variety of social-cognitive mechanisms influence how much individuals choose to invest in group efforts. However, experimental research has been unable to shed light on how social cognitive mechanisms contribute to the where and when of collective action. We build and test a computational model of human behavior in Clean Up, a social dilemma task popular in multi-agent reinforcement learning research. We show that human groups effectively cooperate in Clean Up when they can identify group members and track reputations over time, but fail to organize under conditions of anonymity. A multi-agent reinforcement learning model of reputation demonstrates the same difference in cooperation under conditions of identifiability and anonymity. In addition, the model accurately predicts spatial and temporal patterns of group behavior: in this public goods dilemma, the intrinsic motivation for reputation catalyzes the development of a non-territorial, turn-taking strategy to coordinate collective action.