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.

翻译：实验室实验广泛探索了这一进程的第一部分,表明各种社会认知机制影响着个人对集体努力的投资;然而,实验性研究无法揭示社会认知机制如何有助于集体行动的地点和时间;我们在清洁组织中建立和试验一个人类行为的计算模型,这是多试剂强化学习研究中受欢迎的社会困境任务;我们表明,人类团体在清洁组织中有效合作,只要它们能够识别小组成员并跟踪其声誉,但未能在匿名条件下组织起来;多剂强化的声誉学习模式显示了在识别性和匿名性条件下合作的相同差异;此外,该模型准确地预测了群体行为的空间和时间模式:在这种公益的两难境地中,声誉的内在动机催化了非地区性、转变性战略的协调集体行动。