Social comparison shapes the evolution of cooperation in structured populations

Human cooperation unfolds in social environments where individuals influence each other through payoff-based learning and social comparison, the tendency to evaluate fitness relative to others. However, it is still unclear how social comparison and population structure jointly shape cooperation. Here, we incorporate social comparison theory into evolutionary dynamics on structured populations, letting fitness depend on individual and neighbour payoffs weighted by a comparison parameter. Under weak selection, we derive conditions favoring cooperation and find that the proposed comparison nonlinearly reshapes the critical benefit-to-cost ratio. Even one individual applying this protocol can affect the population, especially in heterogeneous networks. When comparison tendencies vary, the full distribution, not just the mean, determines evolutionary outcomes. Using a swarm-intelligence-based framework across typical and empirical networks, we identify cooperation-maximizing patterns: optimal states exhibit heterogeneous comparison tendencies, yet collectively align toward assimilative development. These results provide a basis for designing social incentives that harness comparison to promote collective cooperation in human groups.