# Correlated equilibrium

Last updated
Correlated equilibrium
A solution concept in game theory
Relationship
Superset of Nash equilibrium
Significance
Proposed by Robert Aumann
Example Chicken

In game theory, a correlated equilibrium is a solution concept that is more general than the well known Nash equilibrium. It was first discussed by mathematician Robert Aumann in 1974. [1] [2] The idea is that each player chooses their action according to their observation of the value of the same public signal. A strategy assigns an action to every possible observation a player can make. If no player would want to deviate from the recommended strategy (assuming the others don't deviate), the distribution is called a correlated equilibrium.

Game theory is the study of mathematical models of strategic interaction among rational decision-makers. It has applications in all fields of social science, as well as in logic, systems science, and computer science. Originally, it addressed zero-sum games, in which each participant's gains or losses are exactly balanced by those of the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

In game theory, a solution concept is a formal rule for predicting how a game will be played. These predictions are called "solutions", and describe which strategies will be adopted by players and, therefore, the result of the game. The most commonly used solution concepts are equilibrium concepts, most famously Nash equilibrium.

In game theory, the Nash equilibrium, named after the mathematician John Forbes Nash Jr., is a proposed solution of a non-cooperative game involving two or more players in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only their own strategy.

## Formal definition

An ${\displaystyle N}$-player strategic game ${\displaystyle \displaystyle (N,A_{i},u_{i})}$ is characterized by an action set ${\displaystyle A_{i}}$ and utility function ${\displaystyle u_{i}}$ for each player ${\displaystyle i}$. When player ${\displaystyle i}$ chooses strategy ${\displaystyle a_{i}\in A_{i}}$ and the remaining players choose a strategy profile described by the ${\displaystyle N-1}$-tuple ${\displaystyle a_{-i}}$, then player ${\displaystyle i}$'s utility is ${\displaystyle \displaystyle u_{i}(a_{i},a_{-i})}$.

A strategy modification for player ${\displaystyle i}$ is a function ${\displaystyle \phi _{i}\colon A_{i}\to A_{i}}$. That is, ${\displaystyle \phi _{i}}$ tells player ${\displaystyle i}$ to modify his behavior by playing action ${\displaystyle \phi _{i}(a_{i})}$ when instructed to play ${\displaystyle a_{i}}$.

Let ${\displaystyle (\Omega ,\pi )}$ be a countable probability space. For each player ${\displaystyle i}$, let ${\displaystyle P_{i}}$ be his information partition, ${\displaystyle q_{i}}$ be ${\displaystyle i}$'s posterior and let ${\displaystyle s_{i}\colon \Omega \rightarrow A_{i}}$, assigning the same value to states in the same cell of ${\displaystyle i}$'s information partition. Then ${\displaystyle ((\Omega ,\pi ),P_{i},s_{i})}$ is a correlated equilibrium of the strategic game ${\displaystyle (N,A_{i},u_{i})}$ if for every player ${\displaystyle i}$ and for every strategy modification ${\displaystyle \phi _{i}}$:

In mathematics, a countable set is a set with the same cardinality as some subset of the set of natural numbers. A countable set is either a finite set or a countably infinite set. Whether finite or infinite, the elements of a countable set can always be counted one at a time and, although the counting may never finish, every element of the set is associated with a unique natural number.

In probability theory, a probability space or a probability triple is a mathematical construct that models a real-world process consisting of states that occur randomly. A probability space is constructed with a specific kind of situation or experiment in mind. One proposes that each time a situation of that kind arises, the set of possible outcomes is the same and the probabilities are also the same.

In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account. Similarly, the posterior probability distribution is the probability distribution of an unknown quantity, treated as a random variable, conditional on the evidence obtained from an experiment or survey. "Posterior", in this context, means after taking into account the relevant evidence related to the particular case being examined. For instance, there is a ("non-posterior") probability of a person finding buried treasure if they dig in a random spot, and a posterior probability of finding buried treasure if they dig in a spot where their metal detector rings.

${\displaystyle \sum _{\omega \in \Omega }q_{i}(\omega )u_{i}(s_{i}(\omega ),s_{-i}(\omega ))\geq \sum _{\omega \in \Omega }q_{i}(\omega )u_{i}\left(\phi _{i}\left(s_{i}(\omega )\right),s_{-i}(\omega )\right)}$

In other words, ${\displaystyle ((\Omega ,\pi ),P_{i})}$ is a correlated equilibrium if no player can improve his or her expected utility via a strategy modification.

## An example

 Dare Chicken out Dare 0, 0 7, 2 Chicken out 2, 7 6, 6 A game of Chicken

Consider the game of chicken pictured. In this game two individuals are challenging each other to a contest where each can either dare or chicken out. If one is going to Dare, it is better for the other to chicken out. But if one is going to chicken out it is better for the other to Dare. This leads to an interesting situation where each wants to dare, but only if the other might chicken out.

In this game, there are three Nash equilibria. The two pure strategy Nash equilibria are (D, C) and (C, D). There is also a mixed strategy equilibrium where each player Dares with probability 1/3.

Now consider a third party (or some natural event) that draws one of three cards labeled: (C, C), (D, C), and (C, D), with the same probability, i.e. probability 1/3 for each card. After drawing the card the third party informs the players of the strategy assigned to them on the card (but not the strategy assigned to their opponent). Suppose a player is assigned D, he would not want to deviate supposing the other player played their assigned strategy since he will get 7 (the highest payoff possible). Suppose a player is assigned C. Then the other player will play C with probability 1/2 and D with probability 1/2. The expected utility of Daring is 7(1/2) + 0(1/2) = 3.5 and the expected utility of chickening out is 2(1/2) + 6(1/2) = 4. So, the player would prefer chickening out.

Since neither player has an incentive to deviate, this is a correlated equilibrium. The expected payoff for this equilibrium is 7(1/3) + 2(1/3) + 6(1/3) = 5 which is higher than the expected payoff of the mixed strategy Nash equilibrium.

The following correlated equilibrium has an even higher payoff to both players: Recommend (C, C) with probability 1/2, and (D, C) and (C, D) with probability 1/4 each. Then when a player is recommended to play C, she knows that the other player will play D with (conditional) probability 1/3 and C with probability 2/3, and gets expected payoff 14/3, which is equal to (not less than) the expected payoff when she plays D. In this correlated equilibrium, both players get 5.25 in expectation. It can be shown that this is the correlated equilibrium with maximal sum of expected payoffs to the two players.

## Learning correlated equilibria

One of the advantages of correlated equilibria is that they are computationally less expensive than Nash equilibria. This can be captured by the fact that computing a correlated equilibrium only requires solving a linear program whereas solving a Nash equilibrium requires finding its fixed point completely. [3] Another way of seeing this is that it is possible for two players to respond to each other's historical plays of a game and end up converging to a correlated equilibrium. [4]

## Related Research Articles

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## References

1. Aumann, Robert (1974). "Subjectivity and correlation in randomized strategies". Journal of Mathematical Economics . 1 (1): 67–96. CiteSeerX  . doi:10.1016/0304-4068(74)90037-8.
2. Aumann, Robert (1987). "Correlated Equilibrium as an Expression of Bayesian Rationality". Econometrica . 55 (1): 1–18. CiteSeerX  . doi:10.2307/1911154. JSTOR   1911154.
3. Papadimitriou, Christos H.; Roughgarden, Tim (2008). "Computing correlated equilibria in multi-player games". J. ACM. 55 (3): 14:1–14:29. CiteSeerX  . doi:10.1145/1379759.1379762.
4. Foster, Dean P.; Vohra, Rakesh V. (1996). "Calibrated Learning and Correlated Equilibrium". Games and Economic Behavior.
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