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A payoff function for a player is a mapping from the cross-product of players' strategy spaces to that player's set of payoffs (normally the set of real numbers, where the number represents a cardinal or ordinal utility—often cardinal in the normal-form representation) of a player, i.e. the payoff function of a player takes as its input a ...
It may contain usage information, categories and other content that is not part of the original template page. Usage [ edit ] This template allows simple construction of 2-player, 2-strategy payoff matrices in game theory and other articles.
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Used in: Battle of the sexes (no parens), Evolutionary stable strategy (no parens), Matching pennies (parens), Mixed strategy (although with ugly spacing), Normal form game (no parens), Payoff dominant equilibrium, Payoff matrix (along with row only), Pure strategy (parens), Solution concept (no parens), Stag hunt (no parens)
Risk dominance and payoff dominance are two related refinements of the Nash equilibrium (NE) solution concept in game theory, defined by John Harsanyi and Reinhard Selten.A Nash equilibrium is considered payoff dominant if it is Pareto superior to all other Nash equilibria in the game. 1 When faced with a choice among equilibria, all players would agree on the payoff dominant equilibrium since ...
The rule goes as follows: if the first payoff number, in the payoff pair of the cell, is the maximum of the column of the cell and if the second number is the maximum of the row of the cell - then the cell represents a Nash equilibrium.
The optional prisoner's dilemma (OPD) game models a situation of conflict involving two players in game theory.It can be seen as an extension of the standard prisoner's dilemma game, where players have the option to "reject the deal", that is, to abstain from playing the game. [1]
Payoff functions, u: Assign a payoff to a player given their type and the action profile. A payoff function, u= (u 1 , . . . , u N ) denotes the utilities of player i Prior, p : A probability distribution over all possible type profiles, where p(t) = p(t 1 , . . . ,t N ) is the probability that Player 1 has type t 1 and Player N has type t N .