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Epsilonequilibrium
Epsilonequilibrium  

A solution concept in game theory  
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In game theory, an epsilonequilibrium, or nearNash equilibrium, is a strategy profile that approximately satisfies the condition of Nash equilibrium. In a Nash equilibrium, no player has an incentive to change his behavior. In an approximate Nash equilibrium, this requirement is weakened to allow the possibility that a player may have a small incentive to do something different. This may still be considered an adequate solution concept, assuming for example status quo bias. This solution concept may be preferred to Nash equilibrium due to being easier to compute, or alternatively due to the possibility that in games of more than 2 players, the probabilities involved in an exact Nash equilibrium need not be rational numbers.^{[1]}
Contents
Definition
There is more than one alternative definition.
The standard definition
Given a game and a real nonnegative parameter <math>\varepsilon</math>, a strategy profile is said to be an <math>\varepsilon</math>equilibrium if it is not possible for any player to gain more than <math>\varepsilon</math> in expected payoff by unilaterally deviating from his strategy^{[2]} Every Nash Equilibrium is equivalent to an <math>\varepsilon</math>equilibrium where <math>\varepsilon = 0</math>.
Formally, let <math>G = (N, A=A_1 \times \dotsb \times A_N, u\colon A \to R^N)</math> be an <math>N</math>player game with action sets <math>A_i</math> for each player <math>i</math> and utility function <math>u</math>. Let <math>u_i (s)</math> denote the payoff to player <math>i</math> when strategy profile <math>s</math> is played. Let <math>\Delta_i</math> be the space of probability distributions over <math>A_i</math>. A vector of strategies <math>\sigma \in \Delta = \Delta_1 \times \dotsb \times \Delta_N</math> is an <math>\varepsilon</math>Nash Equilibrium for <math>G</math> if
 <math>u_i(\sigma)\geq u_i(\sigma_i^',\sigma_{i})\varepsilon</math> for all <math>\sigma_i^' \in \Delta_i, i \in N.</math>
Wellsupported approximate equilibrium
The following definition^{[3]} imposes the stronger requirement that a player may only assign positive probability to a pure strategy <math>a</math> if the payoff of <math>a</math> has expected payoff at most <math>\varepsilon</math> less than the best response payoff. Let <math>x_s</math> be the probability that strategy profile <math>s</math> is played. For player <math>p</math> let <math>S_{p}</math> be strategy profiles of players other than <math>p</math>; for <math>s\in S_{p}</math> and a pure strategy <math>j</math> of <math>p</math> let <math>js</math> be the strategy profile where <math>p</math> plays <math>j</math> and other players play <math>s</math>. Let <math>u_p(s)</math> be the payoff to <math>p</math> when strategy profile <math>s</math> is used. The requirement can be expressed by the formula
 <math>\sum_{s\in S_{p}}u_p(js)x_s > \varepsilon+\sum_{s\in S_{p}}u_p(j's)x_s \Longrightarrow x^p_{j'} = 0.</math>
Results
The existence of a polynomialtime approximation scheme (PTAS) for εNash equilibria is equivalent to the question of whether there exists one for εwellsupported approximate Nash equilibria,^{[4]} but the existence of a PTAS remains an open problem. For constant values of ε, polynomialtime algorithms for approximate equilibria are known for lower values of ε than are known for wellsupported approximate equilibria. For games with payoffs in the range [0,1] and ε=0.3393, εNash equilibria can be computed in polynomial time^{[5]} For games with payoffs in the range [0,1] and ε=2/3, εwellsupported equilibria can be computed in polynomial time^{[6]}
Example
The notion of εequilibria is important in the theory of stochastic games of potentially infinite duration. There are simple examples of stochastic games with no Nash equilibrium but with an εequilibrium for any ε strictly bigger than 0.
Perhaps the simplest such example is the following variant of Matching Pennies, suggested by Everett. Player 1 hides a penny and Player 2 must guess if it is heads up or tails up. If Player 2 guesses correctly, he wins the penny from Player 1 and the game ends. If Player 2 incorrectly guesses that the penny is heads up, the game ends with payoff zero to both players. If he incorrectly guesses that it is tails up, the game repeats. If the play continues forever, the payoff to both players is zero.
Given a parameter ε > 0, any strategy profile where Player 2 guesses heads up with probability ε and tails up with probability 1ε (at every stage of the game, and independently from previous stages) is an εequilibrium for the game. The expected payoff of Player 2 in such a strategy profile is at least 1ε. However, it is easy to see that there is no strategy for Player 2 that can guarantee an expected payoff of exactly 1. Therefore, the game has no Nash equilibrium.
Another simple example is the finitely repeated prisoner's dilemma for T periods, where the payoff is averaged over the T periods. The only Nash equilibrium of this game is to choose Defect in each period. Now consider the two strategies titfortat and grim trigger. Although neither titfortat nor grim trigger are Nash equilibria for the game, both of them are <math>\epsilon</math>equilibria for some positive <math>\epsilon</math>. The acceptable values of <math>\epsilon</math> depend on the payoffs of the constituent game and on the number T of periods.
In Economics, the concept of a Pure strategy epsilonequilibrium is used when the mixedstrategy approach is seen as unrealistic. In a purestrategy epsilonequilibrium, each player chooses a purestrategy that is within epsilon of its best purestrategy. For example, in the BertrandEdgeworth model, where no purestrategy equilibrium exists, a purestrategy epsilon equilibrium may exist.
References
 ^ V. Bubelis (1979). "On equilibria in finite games". International Journal on Game Theory 8 (2): 65–79. doi:10.1007/bf01768703.
 ^ Nisan, Noam; Roughgarden, Tim; Tardos, Éva; Vazirani, Vijay V. (2007). Algorithmic Game Theory (PDF). Cambridge, UK: Cambridge University Press. p. 45. ISBN 0521872820.
 ^ P.W. Goldberg and C.H. Papadimitriou (2006). "Reducibility Among Equilibrium Problems". 38th Symposium on Theory of Computing. pp. 61–70. doi:10.1145/1132516.1132526.
 ^ C. Daskalakis, P.W. Goldberg and C.H. Papadimitriou (2009). "The Complexity of Computing a Nash Equilibrium". SIAM Journal on Computing 39 (3): 195–259. doi:10.1137/070699652.
 ^ H. Tsaknakis and Paul G. Spirakis (2008). "An optimisation approach for approximate Nash equilibria". Internet Mathematics 5 (4): 365–382. doi:10.1080/15427951.2008.10129172.
 ^ Spyros C. Kontogiannis and Paul G. Spirakis (2010). "Well Supported Approximate Equilibria in Bimatrix Games". Algorithmica 57 (4): 653–667. doi:10.1007/s0045300892276.
 H Dixon Approximate Bertrand Equilibrium in a Replicated Industry, Review of Economic Studies, 54 (1987), pages 47–62.
 H. Everett. "Recursive Games". In H.W. Kuhn and A.W. Tucker, editors. Contributions to the theory of games, vol. III, volume 39 of Annals of Mathematical Studies. Princeton University Press, 1957.
 LeytonBrown, Kevin; Shoham, Yoav (2008), Essentials of Game Theory: A Concise, Multidisciplinary Introduction, San Rafael, CA: Morgan & Claypool Publishers, ISBN 9781598295931. An 88page mathematical introduction; see Section 3.7. Free online at many universities.
 R. Radner. Collusive behavior in noncooperative epsilon equilibria of oligopolies with long but finite lives, Journal of Economic Theory, 22, 121157, 1980.
 Shoham, Yoav; LeytonBrown, Kevin (2009), Multiagent Systems: Algorithmic, GameTheoretic, and Logical Foundations, New York: Cambridge University Press, ISBN 9780521899437. A comprehensive reference from a computational perspective; see Section 3.4.7. Downloadable free online.
 S.H. Tijs. Nash equilibria for noncooperative nperson games in normal form, Siam Review, 23, 225237, 1981.