{"id":2142,"date":"2025-03-01T07:02:27","date_gmt":"2025-03-01T07:02:27","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/03\/01\/i-wont-change-unless-you-do\/"},"modified":"2025-03-01T07:02:27","modified_gmt":"2025-03-01T07:02:27","slug":"i-wont-change-unless-you-do","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/03\/01\/i-wont-change-unless-you-do\/","title":{"rendered":"I Won\u2019t Change Unless You Do"},"content":{"rendered":"

I Won\u2019t Change Unless You Do
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In Game Theory<\/a>, how can players ever come to an end if there still might be a better option to decide for? Maybe one player still wants to change their decision. But if they do, maybe the other player wants to change too. How can they ever hope to escape from this vicious circle? To solve this problem, the concept of a Nash equilibrium, which I will explain in this article, is fundamental to game theory.<\/p>\n

This article is the second part of a four-chapter series on game theory. If you haven\u2019t checked out the first chapter<\/a> yet, I\u2019d encourage you to do that to get familiar with the main terms and concepts of game theory. If you did so, you are prepared for the next steps of our journey through game theory. Let\u2019s go!<\/p>\n

Finding the solution<\/strong><\/h3>\n
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Finding a solution to a game in game theory can be tricky sometimes. Photo by Mel Poole<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

We will now try to find a solution for a game in game theory. A solution<\/strong> is a set of actions, where each player maximizes their utility<\/strong> and therefore behaves rationally. That does not necessarily mean, that each player wins the game, but that they do the best they can do, given that they don\u2019t know what the other players will do. Let\u2019s consider the following game:<\/p>\n

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If you are unfamiliar with this matrix-notation, you might want to take a look back at Chapter 1 and refresh your memory. Do you remember that this matrix gives you the reward for each player given a specific pair of actions? For example, if player 1 chooses action Y and player 2 chooses action B, player 1 will get a reward of 1 and player 2 will get a reward of 3.\u00a0<\/p>\n

Okay, what actions should the players decide for now? Player 1 does not know what player 2 will do, but they can still try to find out what would be the best action depending on player 2\u2019s choice. If we compare the utilities of actions Y and Z (indicated by the blue and red boxes in the next figure), we notice something interesting: If player 2 chooses action A (first column of the matrix), player 1 will get a reward of 3, if they choose action Y and a reward of 2, if they choose action Z, so action Y is better in that case. But what happens, if player 2 decides for action B (second column)? In that case, action Y gives a reward of 1 and action Z gives a reward of 0, so Y is better than Z again. And if player 2 chooses action C (third column), Y is still better than Z (reward of 2 vs. reward of 1). That means, that player 1 should never use action Z, because action Y is always better.<\/p>\n

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We compare the rewards for player 1for actions Y and Z.<\/p>\n

With the aforementioned considerations, player 2 can anticipate, that player 1 would never use action Z and hence player 2 doesn\u2019t have to care about the rewards that belong to action Z. This makes the game much smaller, because now there are only two options left for player 1, and this also helps player 2 decide for their action.<\/p>\n

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We found out, that for player 1 Y is always better than Z, so we don\u2019t consider Z anymore.<\/p>\n

If we look at the truncated game, we see, that for player 2, option B is always better than action A. If player 1 chooses X, action B (with a reward of 2) is better than option A (with a reward of 1), and the same applies if player 1 chooses action Y. Note that this would not be the case if action Z was still in the game. However, we already saw that action Z will never be played by player 1 anyway.<\/p>\n

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We compare the rewards for player 2 for actions A and B.<\/p>\n

As a consequence, player 2 would never use action A. Now if player 1 anticipates that player 2 never uses action A, the game becomes smaller again and fewer options have to be considered.<\/p>\n

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We saw, that for player 2 action B is always better than action A, so we don\u2019t have to consider A anymore.<\/p>\n

We can easily continue in a likewise fashion and see that for player 1, X is now always better than Y (2>1 and 4>2). Finally, if player 1 chooses action A, player 2 will choose action B, which is better than C (2>0). In the end, only the action X (for player 1) and B (for player 2) are left. That is the solution of our game:<\/p>\n

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In the end, only one option remains, namely player 1 using X and player 2 using B.<\/p>\n

It would be rational for player 1 to choose action X and for player 2 to choose action B. Note that we came to that conclusion without exactly knowing<\/em> what the other player would do. We just anticipated that some actions would never be taken, because they are always worse than other actions. Such actions are called strictly dominated<\/strong>. For example, action Z is strictly dominated by action Y, because Y is always better than Z.\u00a0<\/p>\n

The best answer<\/strong><\/h3>\n
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Scrabble is one of those games, where searching for the best answer can take ages. Photo by Freysteinn G. Jonsson<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

Such strictly dominated actions do not always exist, but there is a similar concept that is of importance for us and is called a best answer.<\/strong> Say we know which action the other player chooses. In that case, deciding on an action becomes very easy: We just take the action that has the highest reward. If player 1 knew that player 2 chose option A, the best answer for player 1 would be Y, because Y has the highest reward in that column. Do you see how we always searched for the best answers before? For each possible action of the other player we searched for the best answer, if the other player chose that action. More formally, player i\u2019s best answer to a given set of actions of all other players is the action of player 1 which maximises the utility given the other players\u2019 actions. Also be aware, that a strictly dominated action can never be a best answer.\u00a0<\/p>\n

Let us come back to a game we introduced in the first chapter: The prisoners\u2019 dilemma. What are the best answers here?<\/p>\n

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Prisoners\u2019 dilemma<\/figcaption><\/figure>\n

How should player 1 decide, if player 2 confesses or denies? If player 2 confesses, player 1 should confess as well, because a reward of -3 is better than a reward of -6. And what happens, if player 2 denies? In that case, confessing is better again, because it would give a reward of 0, which is better than a reward of -1 for denying. That means, for player 1 confessing is the best answer for both actions of player 2. Player 1 doesn\u2019t have to worry about the other player\u2019s actions at all but should always confess. Because of the game\u2019s symmetry, the same applies to player 2. For them, confessing is also the best answer, no matter what player 1 does.\u00a0<\/p>\n

The Nash Equilibrium<\/strong><\/h3>\n
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The Nash equilibrium is somewhat like the master key that allows us to solve game-theoretic problems. Researchers were very happy when they found it. Photo by rc.xyz NFT gallery<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

If all players play their best answer, we have reached a solution of the game that is called a Nash Equilibrium<\/a><\/strong>. This is a key concept in game theory, because of an important property: In a Nash Equilibrium, no player has any reason to change their action, unless any other player does<\/strong>. That means all players are as happy as they can be in the situation and they wouldn\u2019t change, even if they could. Consider the prisoner\u2019s dilemma from above: The Nash equilibrium is reached when both confess. In this case, no player would change his action without the other. They could become better if both<\/strong> changed their action and decided to deny, but since they can\u2019t communicate, they don\u2019t expect any change from the other player and so they don\u2019t change themselves either.\u00a0<\/p>\n

You may wonder if there is always a single Nash equilibrium for each game. Let me tell you there can also be multiple ones, as in the Bach vs. Stravinsky game that we already got to know in Chapter 1<\/a>:<\/p>\n

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Bach vs. Stravinsky<\/figcaption><\/figure>\n

This game has two Nash equilibria: (Bach, Bach) and (Stravinsky, Stravinsky). In both scenarios, you can easily imagine that there is no reason for any player to change their action in isolation. If you sit in the Bach concerto with your friend, you would not leave your seat to go to the Stravinsky concerto alone, even if you favour Stravinsky over Bach. In a likewise fashion, the Bach fan wouldn\u2019t go away from the Stravinsky concerto if that meant leaving his friend alone. In the remaining two scenarios, you would think differently though: If you were in the Stravinsky concerto alone, you would want to get out there and join your friend in the Bach concerto. That is, you would change your action even if the other player doesn\u2019t change theirs. This tells you, that the scenario you have been in was not<\/strong> a Nash equilibrium.\u00a0<\/p>\n

However, there can also be games that have no Nash equilibrium at all. Imagine you are a soccer keeper during a penalty shot. For simplicity, we assume you can jump to the left or to the right. The soccer player of the opposing team can also shoot in the left or right corner, and we assume, that you catch the ball if you decide for the same corner as they do and that you don\u2019t catch it if you decide for opposing corners. We can display this game as follows:<\/p>\n

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A game matrix for a penalty shooting.<\/figcaption><\/figure>\n

You won\u2019t find any Nash equilibrium here. Each scenario has a clear winner (reward 1) and a clear loser (reward -1), and hence one of the players will always want to change. If you jump to the right and catch the ball, your opponent will wish to change to the left corner. But then you again will want to change your decision, which will make your opponent choose the other corner again and so on.<\/p>\n

Summary<\/strong><\/h3>\n
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We learned about finding a point of balance, where nobody wants to change anymore. That is a Nash equilibrium. Photo by Eran Menashri<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

This chapter showed how to find solutions for games by using the concept of a Nash equilibrium. Let us summarize, what we have learned so far:\u00a0<\/p>\n