Minimax strategy

What Minimax strategy is

The Minimax strategy is a decision-making technique used in games with two players where each player tries to maximize their own benefit while minimizing the benefit of the opponent. It is a zero-sum game, meaning that one player’s gain is the other player’s loss.

The Minimax strategy involves a series of steps:

  1. Evaluate the available options: Each player must evaluate all the available options and determine which one is the most beneficial for them.
  2. Choose the best option: Based on the evaluation, each player must choose the best option that maximizes their benefit.
  3. Calculate the opponent’s best response: The player must then calculate what their opponent’s best response would be.
  4. Choose the response that minimizes the opponent’s benefit: Based on the opponent’s best response, the player must choose the response that minimizes the opponent’s benefit.
  5. Repeat until a resolution is reached: The players must repeat this process until a resolution is reached and a winner is determined.

Examples

  1. Minimax strategy is used in decision-making when a decision maker needs to make a choice between two or more options. For example, a company may use the minimax strategy when deciding whether to invest in a new project or not. The decision maker would compare the expected returns from the two or more options and then choose the option that has the lowest maximum possible loss (i.e. minimizes the maximum possible loss).

  2. Minimax strategy is also used in game theory to determine the optimal strategy for a player in a game. For example, a chess player may use the minimax strategy when deciding which move to make in a particular chess game. The player would consider all of the possible moves and then choose the move that has the lowest maximum possible loss for the player.

  3. Minimax strategy is used in statistical inference to determine the best model to use for a particular data set. For example, a statistician may use the minimax strategy when deciding which type of regression model to use for a particular data set. The statistician would compare the different models and then choose the one that has the lowest maximum possible loss.

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