# Nash Equilibrium

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## Definition of 'Nash Equilibrium'

In game theory, a Nash equilibrium is a set of strategies for the players in a game such that no player can benefit by unilaterally changing their strategy. In other words, each player is doing as well as they can given the strategies of the other players.

Nash equilibria are important because they represent stable states of a game. Once a Nash equilibrium is reached, no player has any incentive to change their strategy, so the game will remain in that state indefinitely.

Nash equilibria are not always easy to find, and there may be multiple Nash equilibria in a given game. However, finding Nash equilibria is important for understanding the dynamics of games and for developing strategies to win.

One of the most famous examples of a Nash equilibrium is the prisoner's dilemma. In this game, two prisoners are accused of a crime and are held in separate cells. The prosecutor offers each prisoner a deal: if they confess and testify against the other prisoner, they will receive a reduced sentence. If neither prisoner confesses, they will both receive a light sentence. However, if one prisoner confesses and the other does not, the confessor will receive a light sentence and the other prisoner will receive a long sentence.

The prisoner's dilemma is a classic example of a game with multiple Nash equilibria. In this game, there are two Nash equilibria: (1) both prisoners confess, and (2) neither prisoner confesses. In the first equilibrium, both prisoners are worse off than they would be if neither confessed. However, in the second equilibrium, both prisoners are better off than they would be if one prisoner confessed and the other did not.

The prisoner's dilemma illustrates the tension between individual and collective rationality. In this game, it is individually rational for each prisoner to confess, even though it is collectively rational for both prisoners to remain silent.

Nash equilibria are important concepts in game theory because they help us to understand the dynamics of games and to develop strategies to win. They are also important because they can help us to understand the behavior of people in real-world situations.

Nash equilibria are important because they represent stable states of a game. Once a Nash equilibrium is reached, no player has any incentive to change their strategy, so the game will remain in that state indefinitely.

Nash equilibria are not always easy to find, and there may be multiple Nash equilibria in a given game. However, finding Nash equilibria is important for understanding the dynamics of games and for developing strategies to win.

One of the most famous examples of a Nash equilibrium is the prisoner's dilemma. In this game, two prisoners are accused of a crime and are held in separate cells. The prosecutor offers each prisoner a deal: if they confess and testify against the other prisoner, they will receive a reduced sentence. If neither prisoner confesses, they will both receive a light sentence. However, if one prisoner confesses and the other does not, the confessor will receive a light sentence and the other prisoner will receive a long sentence.

The prisoner's dilemma is a classic example of a game with multiple Nash equilibria. In this game, there are two Nash equilibria: (1) both prisoners confess, and (2) neither prisoner confesses. In the first equilibrium, both prisoners are worse off than they would be if neither confessed. However, in the second equilibrium, both prisoners are better off than they would be if one prisoner confessed and the other did not.

The prisoner's dilemma illustrates the tension between individual and collective rationality. In this game, it is individually rational for each prisoner to confess, even though it is collectively rational for both prisoners to remain silent.

Nash equilibria are important concepts in game theory because they help us to understand the dynamics of games and to develop strategies to win. They are also important because they can help us to understand the behavior of people in real-world situations.

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Copyright © 2004-2023, MyPivots. All rights reserved.