  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 leverage multi-agent deep reinforcement learning to model how a
social-cognitive mechanism--specifically, the intrinsic motivation to achieve a
good reputation--steers group behavior toward specific spatial and temporal
strategies for collective action in a social dilemma. We also collect
behavioral data from groups of human participants challenged with the same
dilemma. 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.
