Evolutionarily stable strategy
From Academic Kids

The evolutionarily stable strategy (or ESS; also evolutionary stable strategy) is a central concept in game theory introduced by John Maynard Smith and George R. Price in 1973 (a full account is given by Maynard Smith's 1982 book Evolution and the Theory of Games). It is based on a concept of a population of organisms playing a certain strategy, that a mutant allele that causes organisms to adopt a different strategy cannot invade the population, but will instead be selected out by natural selection.
The concept was based on W.D. Hamilton's (1967) unbeatable strategy; the difference is that an unbeatable strategy is resistant to large migrations of different strategies. Through Hamilton's work on sex ratios the concept can be traced back through Ronald Fisher (1930) and Charles Darwin (1859) (see Edwards, 1998).
Contents 
Definition
An ESS depends on the idea of invasion, where a population of strategyX players is visited by a strategyY player. The new player is said to invade if and only if either (1) following strategy Y, he scores better than the average strategyX player or (2) following strategy Y, he scores equally well against the population as the average strategyX player but does better against Y types than the average strategyX player does against a Y player.
A strategy X is evolutionarily stable if and only if there is no strategy Y that can invade it. That is, anybody bringing a new strategy into a population of strategyX players will fare no better on average than the X players are already doing. (See the closelyrelated Nash equilibrium) ESS is stable in respect to randomly and occasionally occurring invading strategies, thus it is not stable in respect to mass counts of invaders. Similarly, the concept of an ESS only exists under the assumption of infinitely large populations. In finite populations, stochastic effects of genetic drift may force the ESS to become unstable.
Nash equilibria and ESS
Consider the following payoff matrix:
A  B  

A  1, 1  0, 0 
B  0, 0  1, 1 
Both strategies A and B are ESS, since a B player cannot invade a population of A players nor can a A player invade a population of B players. Here the two pure strategy Nash equilibria correspond to the two ESS. In this second game, which also has two pure strategy Nash equilibria, only one corresponds to an ESS:
C  D  

C  1, 1  0, 0 
D  0, 0  0, 0 
Here (D, D) is a Nash equilibrium (since neither player will do better by unilaterally deviating), but it is not an ESS. Consider a C player introduced into a population of D players. The C player does equally well against the population (she scores 0), however the C player does better against herself (she scores 1) than the population does against the C player. Thus, the C player can invade the population of D players.
Even if a game has pure strategy Nash equilibria, it might be the case that none of the strategies are ESS. Consider the following example (known as Chicken):
E  F  

E  0, 0  1, +1 
F  +1, 1  20, 20 
There are two pure strategy Nash equilibria in this game (E, F) and (F, E). However, neither F nor E are ESS. (There is however a polymorphic evolutionary stable state for this game.)
As it turns out, every ESS corresponds to a symmetric pure strategy Nash equilibrium, but (as illustrated in the above examples) not every pure strategy Nash equilibrium is composed of ESS.
Prisoner's dilemma and ESS
Consider a large population of people who, in the iterated prisoner's dilemma, always play Tit for Tat in transactions with each other. (Since almost any transaction requires trust, most transactions can be modelled with the prisoner's dilemma.) If the entire population plays the TitforTat strategy, and a group of newcomers enter the population who prefer the Always Defect strategy (i.e. they try to cheat everyone they meet), the TitforTat strategy will prove more successful, and the defectors will be converted or lose out. Tit for Tat is therefore an ESS, with respect to these two strategies. On the other hand, an island of Always Defect players will be stable against the invasion of a few TitforTat players, but not against a large number of them. The (relatively simple) math behind this can be found in Robert Axelrod's The Evolution of Cooperation, or more briefly here (http://www.urticator.net/essay/2/217.html).
ESS and human behavior
The recent, controversial sciences of sociobiology and now evolutionary psychology attempt to explain animal and human behavior and social structures, largely in terms of evolutionarily stable strategies. For example, in one wellknown 1995 paper (http://www.bbsonline.org/Preprints/OldArchive/bbs.mealey.html) by Linda Mealey, sociopathy (chronic antisocial/criminal behavior) is explained as a combination of two such strategies.
Although ESS were originally considered as stable states for biological evolution, it need not be limited to such contexts. In fact, ESS are stable states for a large class of adaptive dynamics. As a result ESS are used to explain human behavior without presuming that the behavior is necessarily determined by genes
References
 Charles Darwin (1859). On the Origin of Species
 A.W.F. Edwards (1998). Journal of Theoretical Biology
 Ronald Fisher The Genetical Theory of Natural Selection. Clarendon Press, Oxford.
 W.D. Hamilton (1967) "Extraordinary sex ratios." Science
 John Maynard Smith and George R. Price (1973). "The logic of animal conflict." Nature
 John Maynard Smith. (1982) Evolution and the Theory of Games. ISBN 0521288843
 Robert Axelrod (1984) The Evolution of Cooperation ISBN 0465021212
External links
 The Sociobiology of Sociopathy, Mealey, 1995 (http://www.bbsonline.org/Preprints/OldArchive/bbs.mealey.html)
Topics in game theory 

Evolutionarily stable strategy  Mechanism design  Nowin  Winner's curse  Zerosum 
Games: Prisoner's dilemma  Chicken  Stag hunt  Ultimatum game  Matching pennies ... 
Related topics: Mathematics  Economics  Behavioral economics  Evolutionary biology  Evolutionary game theory  Population genetics  Behavioral ecology 
[ edit ] 