Evolutionarily stable state

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A population can be described as being in an evolutionarily stable state when that population's "genetic composition is restored by selection after a disturbance, provided the disturbance is not too large" (Maynard Smith, 1982). [1] This population as a whole can be either monomorphic or polymorphic. [1] This is now referred to as convergent stability. [2]

Contents

History & connection to evolutionary stable strategy

While related to the concept of an evolutionarily stable strategy (ESS), evolutionarily stable states are not identical and the two terms cannot be used interchangeably.

An ESS is a strategy that, if adopted by all individuals within a population, cannot be invaded by alternative or mutant strategies. [1] This strategy becomes fixed in the population because alternatives provide no fitness benefit that would be selected for. In comparison, an evolutionarily stable state describes a population that returns as a whole to its previous composition even after being disturbed. [1] In short: the ESS refers to the strategy itself, uninterrupted and supported through natural selection, while the evolutionarily stable state refers more broadly to a population-wide balance of one or more strategies that may be subjected to temporary change. [3]

The term ESS was first used by John Maynard Smith in an essay from the 1972 book On Evolution. [4] Maynard Smith developed the ESS drawing in part from game theory and Hamilton's work on the evolution of sex ratio. [5] [6] The ESS was later expanded upon in his book Evolution and the Theory of Games in 1982, which also discussed the evolutionarily stable state. [1]

Mixed v. single strategies

There has been variation in how the term is used and exploration of under what conditions an evolutionarily stable state might exist. In 1984, Benhard Thomas compared "discrete" models in which all individuals use only one strategy to "continuous" models in which individuals employ mixed strategies. [3] While Maynard Smith had originally defined an ESS as being a single "uninvadable strategy," Thomas generalized this to include a set of multiple strategies employed by individuals. [1] [3] In other words, a collection of simultaneously present strategies could be considered uninvadable as a group. Thomas noted that evolutionary stability can exist in either model, allowing for an evolutionarily stable state to exist even when multiple strategies are used within the population. [3]

Mathematical formulation & evolutionary game theory

The strategy employed by individuals (or ESS) is thought to depend on fitness: the better the strategy is at supporting fitness, the more likely the strategy is to be used. [5] When it comes to an evolutionarily stable state, all of the strategies used within the population must have equal fitness. [7] While the equilibrium may be disturbed by external factors, the population is considered to be in an evolutionarily stable state if it returns to the equilibrium state after the disturbance. [7]

One of the base mathematical models for identifying an evolutionarily stable state was outlined by Taylor & Jonker in 1978. [7] Their base equilibrium model for ES states stipulates that [3] [7]

A state p is called an ESS (evolutionary stable state) if for every state q ≠ p, if we let p̅ =(1-ε)p + εq (the perturbed state), then F(q|p) < F(p|p̅) for sufficiently small ε>0.

In greater detail, the Taylor & Jonker model can be understood this way [7]

In a game of individuals in competition with each other there are (N) possible strategies available. Thus each individual is using one of these (N) strategies. If we denote each strategy as I we let S_i be the proportion of individuals who are currently using strategy I. Then S=(S_1 -> S_n) is a probability vector (i.e. S ≥ 0 and S_1 + S_2... + S_n = 1) this is called the state vector of the population. Using this the function F(i|s) can be made, F(i|s) refers to the fitness of I in state S. The state vector of the population (S) is not static. The idea behind it is that the more fit a strategy at the moment the more likely it is to be employed in the future, thus the state vector (S) will change. Using game theory we can look how (S) changes over time and try to figure out in what state it has reached an equilibrium. Let K be the set of all probability vectors  of length N, this is the state space of the population. Thus element P in K represents a possible strategy mix. A state P in K is called an equilibrium state if F(i|p) is equal for all pure strategies i for which P_i > 0, That is, supp(p) = {i :p,≠0}. If Q is in K: F(q|p) + (ΣQ_1 x F(i|p). We can see F(q|p) as the expected fitness of an individual using mixed strategy Q against the population in state P. If P is an equilibrium state and the supp(q) is contained in supp(p) then F(q|p) = F(q|p).(supp(p) are the I's for which P_i > 0). Thus a state p is called an ESS (evolutionary stable state) if for every state Q ≠ P, if we let p̅=(1-ε)p + εq (the perturbed state), then F(q|p) < F(p|p̅) for sufficiently small ε>0 [7]

In summary, a state P is evolutionarily stable whenever a small change from P to state p̅ the expected fitness in the perturbed state is less than the expected fitness of the remaining population.

Additional proposals

It has been suggested by Ross Cressman that criteria for evolutionary stability include strong stability, as it would describe evolution of both frequency and density (whereas Maynard Smith's model focused on frequency). [8] Cressman further demonstrated that in habitat selection games modeling only a single species, the ideal free distribution (IFD) is itself an evolutionarily stable state containing mixed strategies. [9]

In evolutionary game theory

Evolutionary game theory as a whole provides a theoretical framework examining interactions of organisms in a system where individuals have repeated interactions within a population that persists on an evolutionarily relevant timescale. [10] This framework can be used to better understand the evolution of interaction strategies and stable states, though many different specific models have been used under this framework. The Nash Equilibrium (NE) and folk theorem are closely related to the evolutionarily stable state. There are various potential refinements proposed to account for different theory games and behavioral models. [11]

For the purpose of predicting evolutionary outcomes, the replicator equation is also a frequently utilized tool. [12] [13] Evolutionarily stable states are often taken as solutions to the replicator equation, here in linear payoff form:

The state is said to be evolutionarily stable if for all in some neighborhood of .

Related Research Articles

An evolutionarily stable strategy (ESS) is a strategy that is impermeable when adopted by a population in adaptation to a specific environment, that is to say it cannot be displaced by an alternative strategy which may be novel or initially rare. Introduced by John Maynard Smith and George R. Price in 1972/3, it is an important concept in behavioural ecology, evolutionary psychology, mathematical game theory and economics, with applications in other fields such as anthropology, philosophy and political science.

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<span class="mw-page-title-main">Handicap principle</span> Hypothesis in evolutionary biology

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In mathematics, the replicator equation is a deterministic monotone non-linear and non-innovative game dynamic used in evolutionary game theory. The replicator equation differs from other equations used to model replication, such as the quasispecies equation, in that it allows the fitness function to incorporate the distribution of the population types rather than setting the fitness of a particular type constant. This important property allows the replicator equation to capture the essence of selection. Unlike the quasispecies equation, the replicator equation does not incorporate mutation and so is not able to innovate new types or pure strategies.

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<span class="mw-page-title-main">Sir Philip Sidney game</span>

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Reciprocal altruism in humans refers to an individual behavior that gives benefit conditionally upon receiving a returned benefit, which draws on the economic concept – ″gains in trade″. Human reciprocal altruism would include the following behaviors : helping patients, the wounded, and the others when they are in crisis; sharing food, implement, knowledge.

References

  1. 1 2 3 4 5 6 Maynard Smith, J.. (1982) Evolution and the Theory of Games. Cambridge University Press. ISBN   0-521-28884-3
  2. ↑ Apaloo, J.; Brown, J. S.; Vincent, T. L. (2009). "Evolutionary game theory: ESS, convergence stability, and NIS". Evolutionary Ecology Research. 11: 489–515. Archived from the original on 2017-08-09. Retrieved 2018-01-10.
  3. 1 2 3 4 5 Thomas, B. (1984). Evolutionary stability: States and strategies. Theoretical Population Biology, 26(1), 49-67. https://doi.org/10.1016/0040-5809(84)90023-6
  4. ↑ Maynard Smith, J. (1972). Game Theory and the Evolution of Fighting. On Evolution. Edinburgh University Press. ISBN   0-85224-223-9.
  5. 1 2 Maynard Smith, J., Price, G. R. (1973). The logic of animal conflict. Nature 246 (5427), 15-18. https://doi.org/10.1038/246015a0
  6. ↑ Maynard Smith, J. (1974). The theory of games and the evolution of animal conflicts. J Theor Biol. 47(1). 209-221.https://doi.org/10.1016/0022-5193(74)90110-6
  7. 1 2 3 4 5 6 Taylor, P. D, Jonker, L. B. (1978). Evolutionarily stable states and Game Dynamics. Mathematical Biosciences 40, 145-156. https://doi.org/10.1016/0025-5564(78)90077-9
  8. ↑ Cressman, R. (1990). Strong stability and density-dependent evolutionarily stable strategies. Journal of Theoretical Biology, 145(3), 319-330. https://doi.org/10.1016/S0022-5193(05)80112-2
  9. ↑ Cressman, R., & Křivan, V. (2010). The ideal free distribution as an evolutionarily stable state in density‐dependent population games. Oikos, 119(8), 1231-1242. https://doi.org/10.1111/j.1600-0706.2010.17845.x
  10. ↑ Cowden, C. C. (2012) Game Theory, Evolutionary Stable Strategies and the Evolution of Biological Interactions. Nature Education Knowledge 3(10):6.
  11. ↑ Li, J., Kendall, G., and John, R. (2015). Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games. IEEE Transactions on Evolutionary Computation, 20(3), 460-469.
  12. ↑ Cressman, R. (2003) Evolutionary Dynamics and Extensive Form Games. The MIT Press. ISBN   9780262033053
  13. ↑ Cressman, R., & Tao, Y. (2014). The replicator equation and other game dynamics. Proceedings of the National Academy of Sciences, 111(Supplement 3), 10810-10817. https://doi.org/10.1073/pnas.1400823111
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