Understanding the Game of Coordination

The coordination game is perhaps the most popular application of Nash equilibrium. Many algorithms were created to perfect the application of determining which combination shows the highest payoff and which option will each player choose in the face of any given situation. It involves two players who are given the option to choose any of the two strategies available to them.


Consider this scenario. The players consist of Player A and Player B. The options consist of Strategy 1 and Strategy 2. Algorithms show that there are four possible scenarios taking place. The first scenario is that both players choose to adopt Strategy 1. The result of this is the highest payoff. But let’s say, Player A adopts Strategy 1 while Player B adopts Strategy 2. If that happens then, Player A will get less payoff and Player B will get more. It’s like Player A only gaining 1 fourth of the total prize while B gets three-fourths. The same happens the other way around. If in case, both players choose to adopt Strategy 2 then, both will only earn half the payoff that both will probably earn if they choose Strategy 1 instead of 2. Take note that if both players choose the same strategy, they will both get a similar payoff.


The coordination game can be exhibited in a stag hunt. Player A and B are hunting either a deer or a rabbit. If both cooperate and chose to hunt the deer together, they will be able to successfully hunt the deer and divide the meat equally. The same goes if they chose to cooperate in hunting the rabbit. The only difference is that the meat is far less than the deer so the payoff is definitely lower. Their combined strategies allowed them to guarantee their payoff.


But let’s say, Player A suspects that B will hunt the rabbit instead of the deer. This mistrust will allow Player A to compete with B for the rabbit which will lead A to hunt the rabbit and the deer will go to B instead. The same happens the other way around.


The coordination game allows analysts to understand how people behave in the face of social cooperation. The algorithms are developed to show them that trusting, and eventual cooperation, can lead to better payoffs.



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