Evolutionary game theory

Evolutionary game theory (EGT) is a framework for modeling strategic interaction in populations where strategies compete, reproduce, and mutate over time. Unlike classical game theory, which assumes rational agents calculating optimal moves, EGT assumes that successful strategies spread through imitation, learning, or genetic inheritance — a dynamic that requires no rationality and no central planner. The central object of analysis is the evolutionarily stable strategy (ESS): a strategy that, when adopted by a population, cannot be invaded by any alternative strategy.

EGT was developed by John Maynard Smith and George Price in the 1970s, drawing on the mathematical tools of population genetics to explain animal conflict, cooperation, and signaling. Its most striking result is that cooperation can emerge and persist in populations of self-interested agents — not because the agents are altruistic, but because the population structure makes defection costly. The prisoner's dilemma ceases to be a tragedy when strategies are embedded in networks with local interaction and limited migration.

The framework has since been applied to economics, political science, and computer science, where it provides the theoretical backbone for understanding how conventions, institutions, and norms evolve without design. EGT is the mathematics of self-organization in strategic environments: it shows how order emerges not from intention but from the accumulation of local interactions under selection pressure.