In game theory and economic theory, a zerosum game is a mathematical representation of a situation in which each participant's gain or loss of utility is exactly balanced by the losses or gains of the utility of the other participants. If the total gains of the participants are added up and the total losses are subtracted, they will sum to zero. Thus, cutting a cake, where taking a larger piece reduces the amount of cake available for others as much as it increases the amount available for that taker, is a zerosum game if all participants value each unit of cake equally (see marginal utility).
In contrast, nonzerosum describes a situation in which the interacting parties' aggregate gains and losses can be less than or more than zero. A zerosum game is also called a strictly competitive game while nonzerosum games can be either competitive or noncompetitive. Zerosum games are most often solved with the minimax theorem which is closely related to linear programming duality,^{[1]} or with Nash equilibrium.
Many people have a cognitive bias towards seeing situations as zerosum, known as zerosum bias.
Choice 1  Choice 2  
Choice 1  −A, A  B, −B 
Choice 2  C, −C  −D, D 
Generic zerosum game 
The zerosum property (if one gains, another loses) means that any result of a zerosum situation is Pareto optimal. Generally, any game where all strategies are Pareto optimal is called a conflict game.^{[2]}
Zerosum games are a specific example of constant sum games where the sum of each outcome is always zero. Such games are distributive, not integrative; the pie cannot be enlarged by good negotiation.
Situations where participants can all gain or suffer together are referred to as nonzerosum. Thus, a country with an excess of bananas trading with another country for their excess of apples, where both benefit from the transaction, is in a nonzerosum situation. Other nonzerosum games are games in which the sum of gains and losses by the players are sometimes more or less than what they began with.
The idea of Pareto optimal payoff in a zerosum game gives rise to a generalized relative selfish rationality standard, the punishingtheopponent standard, where both players always seek to minimize the opponent's payoff at a favorable cost to himself rather to prefer more than less. The punishingtheopponent standard can be used in both zerosum games (e.g. warfare game, chess) and nonzerosum games (e.g. pooling selection games).^{[3]}
For twoplayer finite zerosum games, the different game theoretic solution concepts of minimax theorem which is closely related to linear programming duality,^{[1]} or with Nash equilibrium.
Many people have a cognitive bias towards seeing situations as zerosum, known as zerosum bias.
The zerosum property (if one gains, another loses) means that any result of a zerosum situation is Pareto optimal. Generally, any game where all strategies are Pareto optimal is called a conflict game.^{[2]}
Zerosum games are a specific example of constant sum games where the sum of each outcome is always zero. Such games are distributive, not integrative; the pie cannot be enlarged by good negotiation.
Situations where participants can all gain or suffer together are referred to as nonzerosum. Thus, a country with an excess of bananas trading with another country for their excess of apples, where both benefit from the transaction, is in a nonzerosum situation. Other nonzerosum games are games in which the sum of gains and losses by the players are sometimes more or less than what they began with.
The idea of Pareto optimal payoff in a zerosum game gives rise to a generalized relative selfish rationality standard, the punishingtheopponent standard, where both players always seek to minimize the opponent's payoff at a favorable cost to himself rather to prefer more than less. The punishingtheopponent standard can be used in both zerosum games (e.g. warfare game, chess) and nonzerosum games (e.g. pooling selection games).^{[3]}
For twoplayer finite zerosum games, the different game theoretic solution concepts of Nash equilibrium, minimax, and maximin all give the same solution. If the players are allowed to play a mixed strategy, the game always has an equilibrium.
Blue Red

A  B  C 

1  −30 30

10 −10

[3]
For twoplayer finite zerosum games, the different game theoretic solution concepts of Nash equilibrium, minimax, and maximin all give the same solution. If the players are allowed to play a mixed strategy, the game always has an equilibrium. ExampleThe order of play proceeds as follows: The first player (red) chooses in secret one of the two actions 1 or 2; the second player (blue), unaware of the first player's choice, chooses in secret one of the three actions A, B or C. Then, the choices are revealed and each player's points total is affected according to the payoff for those choices. Example: Red chooses action 2 and Blue chooses action B. When the payoff is allocated, Red gains 20 points and Blue loses 20 points. In this example game, both players know the payoff matrix and attempt to maximize the number of their points. Red could reason as follows: "With action 2, I could lose up to 20 points and can win only 20, and with action 1 I can lose only 10 but can win up to 30, so action 1 looks a lot better." With similar reasoning, Blue would choose action C. If both players take these actions, Red will win 20 points. If Blue anticipates Red's reasoning and choice of action 1, Blue may choose action B, so as to win 10 points. If Red, in turn, anticipates this trick and goes for action 2, this wins Red 20 points. Émile Borel and John von Neumann had the fundamental insight that probability provides a way out of this conundrum. Instead of deciding on a definite action to take, the two players assign probabilities to their respective actions, and then use a random device which, according to these probabilities, chooses an action for them. Each player computes the probabilities so as to minimize the maximum expected pointloss independent of the opponent's strategy. This leads to a linear programming problem with the optimal strategies for each player. This minimax method can compute probably optimal strategies for all twoplayer zerosum games. For the example given above, it turns out that Red should choose action 1 with probability 4/7 and action 2 with probability 3 Émile Borel and John von Neumann had the fundamental insight that probability provides a way out of this conundrum. Instead of deciding on a definite action to take, the two players assign probabilities to their respective actions, and then use a random device which, according to these probabilities, chooses an action for them. Each player computes the probabilities so as to minimize the maximum expected pointloss independent of the opponent's strategy. This leads to a linear programming problem with the optimal strategies for each player. This minimax method can compute probably optimal strategies for all twoplayer zerosum games. For the example given above, it turns out that Red should choose action 1 with probability 4/7 and action 2 with probability 3/7, and Blue should assign the probabilities 0, 4/7, and 3/7 to the three actions A, B, and C. Red will then win 20/7 points on average per game. The Nash equilibrium for a twoplayer, zerosum game can be found by solving a linear programming problem. Suppose a zerosum game has a payoff matrix M where element M_{i,j} is the payoff obtained when the minimizing player chooses pure strategy i and the maximizing player chooses pure strategy j (i.e. the player trying to minimize the payoff chooses the row and the player trying to maximize the payoff chooses the column). Assume every element of M is positive. The game will have at least one Nash equilibrium. The Nash equilibrium can be found (Raghavan 1994, p. 740) by solving the following linear program to find a vector u:
