Evolution of cooperation with temporal information
Strategy learning governs the evolution of collective cooperation in multi-agent systems. Although evolutionary outcomes depend strongly on the information available to agents during strategy learning, most studies treat both the source and amount of that information as fixed over time. In reality, however, individuals are continually exposed to external information that varies over time. Here we develop a general framework for evolutionary dynamics with temporal information, which uses temporal networks to characterize dynamic changes in information available for strategy learning, with time-varying connections determining each agent's information state. Across synthetic and empirical networks, we find that temporal information consistently promotes cooperation relative to their static counterparts. We further demonstrate that this advantage becomes more pronounced as temporal networks become sparser. This pattern arises because sparsification amplifies information heterogeneity among agents, creating unequal access to learning information that can facilitate the spread of cooperative strategies, in sharp contrast to static information formulations where heterogeneity often suppresses cooperation. Guided by this insight, we develop an interpretable algorithm that substantially enhances cooperation across diverse networks by generating learning networks with tunable heterogeneity. Our results identify temporal information as a realistic and broadly applicable mechanism for promoting collective cooperation.