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Evolutionarily stable state

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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) + (ÎŁQ_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-Δ)p + Δq (the perturbed state), then F(q|p) < F(p|p̅) for sufficiently small Δ>0
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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. 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.
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describes a population that returns as a whole to its previous composition even after being disturbed. 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.
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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. When it comes to an evolutionarily stable state, all of the strategies used within the population must have equal fitness. While
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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.
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An ESS is a strategy that, if adopted by all individuals within a population, cannot be invaded by alternative or mutant strategies. This strategy becomes fixed in the population because alternatives provide no fitness benefit that would be selected for. In comparison, an evolutionarily stable state
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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. This framework can be used to better understand the evolution of interaction strategies
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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
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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). Cressman further demonstrated that in habitat selection games modeling only a
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when that population's "genetic composition is restored by selection after a disturbance, provided the disturbance is not too large" (Maynard Smith, 1982). This population as a whole can be either monomorphic or
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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.
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One of the base mathematical models for identifying an evolutionarily stable state was outlined by Taylor & Jonker in 1978. Their base equilibrium model for ES states stipulates that
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A state p is called an ESS (evolutionary stable state) if for every state q ≠ p, if we let p̅ =(1-Δ)p + Δq (the perturbed state), then F(q|p) < F(p|p̅) for sufficiently small Δ>0.
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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.
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For the purpose of predicting evolutionary outcomes, the replicator equation is also a frequently utilized tool. Evolutionarily stable states are often taken as solutions to the
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are closely related to the evolutionarily stable state. There are various potential refinements proposed to account for different theory games and behavioral models.
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Maynard Smith developed the ESS drawing in part from game theory and Hamilton's work on the evolution of sex ratio. The ESS was later expanded upon in his book
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Cressman, R., & Kƙivan, V. (2010). The ideal free distribution as an evolutionarily stable state in density‐dependent population games.
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and stable states, though many different specific models have been used under this framework. The
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Cressman, R. (1990). Strong stability and density-dependent evolutionarily stable strategies.
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Maynard Smith, J.. (1982) Evolution and the Theory of Games. Cambridge University Press.
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Game Theory, Evolutionary Stable Strategies and the Evolution of Biological Interactions
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Cressman, R., & Tao, Y. (2014). The replicator equation and other game dynamics.
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Maynard Smith, J. (1974). The theory of games and the evolution of animal conflicts.
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Taylor, P. D, Jonker, L. B. (1978). Evolutionarily stable states and Game Dynamics.
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Cressman, R. (2003) Evolutionary Dynamics and Extensive Form Games. The MIT Press.
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Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games.
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In greater detail, the Taylor & Jonker model can be understood this way
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Maynard Smith, J., Price, G. R. (1973). The logic of animal conflict.
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Maynard Smith, J. (1972). Game Theory and the Evolution of Fighting.
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Thomas, B. (1984). Evolutionary stability: States and strategies.
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in 1982, which also discussed the evolutionarily stable state.
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History & connection to evolutionary stable strategy
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Mathematical formulation & evolutionary game theory
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Proceedings of the National Academy of Sciences, 111
36:. This is now referred to as convergent stability. 1702: 1661: 1443: 1387: 1169: 1071: 973: 831: 730: 406: 342: 313: 278: 246: 652:IEEE Transactions on Evolutionary Computation, 20 457:Apaloo, J.; Brown, J. S.; Vincent, T. L. (2009). 620:https://doi.org/10.1111/j.1600-0706.2010.17845.x 286:is said to be evolutionarily stable if for all 707: 604:https://doi.org/10.1016/S0022-5193(05)80112-2 27:A population can be described as being in an 8: 588:https://doi.org/10.1016/0025-5564(78)90077-9 561:https://doi.org/10.1016/0022-5193(74)90110-6 505:https://doi.org/10.1016/0040-5809(84)90023-6 407:{\displaystyle x^{T}Ax<{\hat {x}}^{T}Ax} 714: 700: 692: 647:Li, J., Kendall, G., and John, R. 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Edinburgh University Press. 386: 334: 305: 270: 147:, here in linear payoff form: 46:evolutionarily stable strategy 1: 808:Simultaneous action selection 683:(Supplement 3), 10810-10817. 463:Evolutionary Ecology Research 1745:List of games in game theory 920:Quantal response equilibrium 910:Perfect Bayesian equilibrium 845:Bayes correlated equilibrium 636:Nature Education Knowledge 3 1214:Optional prisoner's dilemma 940:Self-confirming equilibrium 584:Mathematical Biosciences 40 124:In evolutionary game theory 29:evolutionarily stable state 1797: 1679:Principal variation search 1395:Aumann's agreement theorem 1058:Strategy-stealing argument 965:Trembling hand equilibrium 895:Markov perfect equilibrium 890:Mertens-stable equilibrium 343:{\displaystyle {\hat {x}}} 279:{\displaystyle {\hat {x}}} 73:Mixed v. single strategies 1715:Combinatorial game theory 1374:Princess and monster game 925:Quasi-perfect equilibrium 850:Bayesian Nash equilibrium 469:: 489–515. Archived from 18:Evolutionary stable state 1781:Evolutionary game theory 1730:Evolutionary game theory 1463:Antoine Augustin Cournot 1349:Guess 2/3 of the average 1146:Strictly determined game 935:Satisfaction equilibrium 753:Escalation of commitment 321:in some neighborhood of 129:Evolutionary game theory 1735:Glossary of game theory 1334:Stackelberg competition 955:Strong Nash equilibrium 118:ideal free distribution 1760:Tragedy of the commons 1740:List of game theorists 1720:Confrontation analysis 1430:Sprague–Grundy theorem 945:Sequential equilibrium 865:Correlated equilibrium 408: 344: 315: 280: 248: 105: 96: 1533:Jean-François Mertens 630:Cowden, C. C. 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Levine 1478:Daniel Kahneman 1445: 1439: 1415:Negamax theorem 1405:Minimax theorem 1383: 1344:Diner's dilemma 1199:All-pay auction 1165: 1151:Stochastic game 1103:Mean-field game 1074: 1067: 1033:Markov strategy 969: 835: 827: 798:Sequential game 783:Information set 768:Game complexity 738:Congestion game 726: 720: 690: 678: 674: 662: 658: 646: 642: 629: 625: 613: 609: 597: 593: 581: 566: 554: 550: 543:(5427), 15-18. 538: 531: 514: 510: 498: 485: 476: 474: 456: 455: 451: 439: 424: 420: 379: 360: 355: 354: 323: 322: 288: 287: 259: 258: 220: 199: 195: 194: 193: 189: 179: 160: 152: 151: 126: 113: 84: 75: 42: 23: 22: 15: 12: 11: 5: 1794: 1792: 1784: 1783: 1773: 1772: 1766: 1765: 1763: 1762: 1757: 1752: 1747: 1742: 1737: 1732: 1727: 1722: 1717: 1712: 1706: 1704: 1700: 1699: 1697: 1696: 1691: 1686: 1681: 1676: 1671: 1665: 1663: 1659: 1658: 1656: 1655: 1650: 1645: 1640: 1635: 1630: 1625: 1620: 1618:Robert Axelrod 1615: 1610: 1605: 1600: 1595: 1593:Olga Bondareva 1590: 1585: 1583:Melvin Dresher 1580: 1575: 1573:Leonid Hurwicz 1570: 1565: 1560: 1555: 1550: 1545: 1540: 1535: 1530: 1525: 1520: 1515: 1510: 1508:Harold W. 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Index

Evolutionary stable state
polymorphic
evolutionarily stable strategy
John Maynard Smith
Evolution and the Theory of Games
ideal free distribution
Evolutionary game theory
Nash Equilibrium
folk theorem
replicator equation






ISBN
0-521-28884-3
"Evolutionary game theory: ESS, convergence stability, and NIS"
the original





https://doi.org/10.1016/0040-5809(84)90023-6
ISBN
0-85224-223-9

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