Evolutionary game theory is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and analytics into which Darwinian competition can be modelled.
Evolutionary game theory (EGT) is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and analytics into which Darwinian competition can be modelled. It originated in 1973 with John Maynard Smith and George R. Price's formalisation of contests, analysed as strategies, and the mathematical criteria that can be used to predict the results of competing strategies.
Evolutionary game theory differs from classical game theory in focusing more on the dynamics of strategy change. This is influenced by the frequency of the competing strategies in the population.
Evolutionary game theory has helped to explain the basis of altruistic behaviours in Darwinian evolution. It has in turn become of interest to economists, sociologists, anthropologists, and philosophers.
Evolutionary game theory analyses Darwinian mechanisms with a system model with three main components – Population, Game, and Replicator Dynamics. The system process has four phases:
The model (as evolution itself) deals with a Population ($P_n$). The population will exhibit Variation among Competing individuals. In the model this competition is represented by the Game.
The Game tests the strategies of the individuals under the "rules of the game". These rules produce different payoffs – in units of Fitness (the production rate of offspring). The contesting individuals meet in pairwise contests with others, normally in a highly mixed distribution of the population. The mix of strategies in the population affects the payoff results by altering the odds that any individual may meet up in contests with various strategies. The individuals leave the game pairwise contest with a resulting fitness determined by the contest outcome, represented in a Payoff Matrix.
Based on this resulting fitness each member of the population then undergoes replication or culling determined by the exact mathematics of the Replicator Dynamics Process. This overall process then produces a New Generation $P_{n+1}$. Each surviving individual now has a new fitness level determined by the game result.
The new generation then takes the place of the previous one and the cycle repeats. The population mix may converge to an Evolutionarily Stable State that cannot be invaded by any mutant strategy.
Source: Wikipedia
