Person:

Krasnow, Max

Humans are often generous, even towards strangers encountered by chance and even in the absence of any explicit information suggesting they will meet again. Because game theoretic analyses typically conclude that a psychology designed for direct reciprocity should defect in such situations, many have concluded that alternative explanations for human generosity— explanations beyond direct reciprocity—are necessary. However, human cooperation evolved within a material and informational ecology: Simply adding consideration of one minimal ecological relationship to the analysis of reciprocity brings theory and observation closer together, indicating that ecology-free analyses of cooperation can be fragile. Using simulations, we show that the autocorrelation of an individual’s location over time means that even a chance encounter with an individual predicts an increased probability of a future encounter with that same individual. We discuss how a psychology designed for such an ecology may be expected to often cooperate even in apparently one-shot situations.

Are humans too generous and too punitive? Many researchers have concluded that classic theories of social evolution (e.g., direct reciprocity, reputation) are not sufficient to explain human cooperation; instead, group selection theories are needed. We think such a move is premature. The leap to these models has been made by moving directly from thinking about selection pressures to predicting patterns of behavior and ignoring the intervening layer of evolved psychology that must mediate this connection. In real world environments, information processing is a non-trivial problem and details of the ecology can dramatically constrain potential solutions, often enabling particular heuristics to be efficient and effective. We argue that making the intervening layer of psychology explicit resolves decades-old mysteries in the evolution of cooperation and punishment.

Humans everywhere cooperate in groups to achieve benefits not attainable by individuals. Individual effort is often not automatically tied to a proportionate share of group benefits. This decoupling allows for free-riding, a strategy that (absent countermeasures) outcompetes cooperation. Empirically and formally, punishment potentially solves the evolutionary puzzle of group cooperation. Nevertheless, standard analyses appear to show that punishment alone is insufficient, because second-order free riders (those who cooperate but do not punish) can be shown to outcompete punishers. Consequently, many have concluded that other processes, such as cultural or genetic group selection, are required. Here, we present a series of agent-based simulations that show that group cooperation sustained by punishment easily evolves by individual selection when you introduce into standard models more biologically plausible assumptions about the social ecology and psychology of ancestral humans. We relax three unrealistic assumptions of past models. First, past models assume all punishers must punish every act of free riding in their group. We instead allow punishment to be probabilistic, meaning punishers can evolve to only punish some free riders some of the time. This drastically lowers the cost of punishment as group size increases. Second, most models unrealistically do not allow punishment to recruit labor; punishment merely reduces the punished agent’s fitness. We instead realistically allow punished free riders to cooperate in the future to avoid punishment. Third, past models usually restrict agents to interact in a single group their entire lives. We instead introduce realistic social ecologies in which agents participate in multiple, partially overlapping groups. Because of this, punitive tendencies are more expressed and therefore more exposed to natural selection. These three moves toward greater model realism reveal that punishment and cooperation easily evolve by direct selection—even in sizeable groups.