Zero-Sum Games and Minimax Strategies
Test your understanding of Zero-Sum Games and Minimax Strategies, fundamental concepts in Game Theory.
Questions
In a zero-sum game, the gains of one player are:
- Exactly equal to the losses of the other player
- Always greater than the losses of the other player
- Always less than the losses of the other player
- Unrelated to the losses of the other player
The minimax strategy in a zero-sum game aims to:
- Maximize the player's own gains
- Minimize the player's own losses
- Maximize the difference between the player's gains and losses
- Minimize the difference between the player's gains and losses
In a zero-sum game, a Nash equilibrium is a situation where:
- Neither player has an incentive to change their strategy
- Both players have an incentive to change their strategy
- One player has an incentive to change their strategy, but the other does not
- Both players have an incentive to cooperate with each other
Consider a zero-sum game with a payoff matrix (A). The minimax value of the game is:
- \(max_{i \in I} min_{j \in J} a_{ij}\)
- \(min_{i \in I} max_{j \in J} a_{ij}\)
- \(max_{i \in I} max_{j \in J} a_{ij}\)
- \(min_{i \in I} min_{j \in J} a_{ij}\)
In a zero-sum game, if the minimax value is equal to the maximin value, then:
- The game has a unique Nash equilibrium
- The game has multiple Nash equilibria
- The game has no Nash equilibrium
- The game is not a zero-sum game
Consider a zero-sum game with a payoff matrix (A). The maximin value of the game is:
- \(max_{i \in I} min_{j \in J} a_{ij}\)
- \(min_{i \in I} max_{j \in J} a_{ij}\)
- \(max_{i \in I} max_{j \in J} a_{ij}\)
- \(min_{i \in I} min_{j \in J} a_{ij}\)
In a zero-sum game, if the minimax value is greater than the maximin value, then:
- The game has a unique Nash equilibrium
- The game has multiple Nash equilibria
- The game has no Nash equilibrium
- The game is not a zero-sum game
Consider a zero-sum game with a payoff matrix (A). The saddle point of the game is a pair of strategies ((i^, j^)) such that:
- \(a_{i^*j^*} \ge a_{ij} \quad \forall i \in I, j \in J\)
- \(a_{i^*j^*} \le a_{ij} \quad \forall i \in I, j \in J\)
- \(a_{i^*j^*} \ge a_{ij} \quad \forall i \in I\)
- \(a_{i^*j^*} \le a_{ij} \quad \forall j \in J\)
In a zero-sum game, if the game has a saddle point, then:
- The minimax value is equal to the maximin value
- The minimax value is greater than the maximin value
- The minimax value is less than the maximin value
- The minimax value is unrelated to the maximin value
Consider a zero-sum game with a payoff matrix (A). The value of the game is:
- \(max_{i \in I} min_{j \in J} a_{ij}\)
- \(min_{i \in I} max_{j \in J} a_{ij}\)
- \(max_{i \in I} max_{j \in J} a_{ij}\)
- \(min_{i \in I} min_{j \in J} a_{ij}\)
In a zero-sum game, if the game has multiple Nash equilibria, then:
- The minimax value is equal to the maximin value
- The minimax value is greater than the maximin value
- The minimax value is less than the maximin value
- The minimax value is unrelated to the maximin value
Consider a zero-sum game with a payoff matrix (A). If the game has a unique Nash equilibrium, then:
- The minimax value is equal to the maximin value
- The minimax value is greater than the maximin value
- The minimax value is less than the maximin value
- The minimax value is unrelated to the maximin value
In a zero-sum game, if the minimax value is less than the maximin value, then:
- The game has a unique Nash equilibrium
- The game has multiple Nash equilibria
- The game has no Nash equilibrium
- The game is not a zero-sum game
Consider a zero-sum game with a payoff matrix (A). If the game has no Nash equilibrium, then:
- The minimax value is equal to the maximin value
- The minimax value is greater than the maximin value
- The minimax value is less than the maximin value
- The minimax value is unrelated to the maximin value