Bayes correlated equilibrium explained

Bayes correlated equilibrium
Supersetof:Correlated equilibrium, Bayesian Nash equilibrium
Discoverer:Dirk Bergemann, Stephen Morris

In game theory, a Bayes correlated equilibrium is a solution concept for static games of incomplete information. It is both a generalization of the correlated equilibrium perfect information solution concept to bayesian games, and also a broader solution concept than the usual Bayesian Nash equilibrium thereof. Additionally, it can be seen as a generalized multi-player solution of the Bayesian persuasion information design problem.

Intuitively, a Bayes correlated equilibrium allows for players to correlate their actions in a way such that no player has an incentive to deviate for every possible type they may have. It was first proposed by Dirk Bergemann and Stephen Morris.[1]

Formal definition

Preliminaries

Let

I

be a set of players, and

\Theta

a set of possible states of the world. A game is defined as a tuple

G=\langle(Ai,ui)i,\Theta,\psi\rangle

, where

Ai

is the set of possible actions (with

A=\prodiAi

) and

ui:A x \ThetaR

is the utility function for each player, and

\psi\in\Delta++(\Theta)

is a full support common prior over the states of the world.

An information structure is defined as a tuple

S=\langle(Ti)i,\pi\rangle

, where

Ti

is a set of possible signals (or types) each player can receive (with

T=\prodiTi

), and

\pi:\Theta\Delta(T)

is a signal distribution function, informing the probability

\pi(t|\theta)

of observing the joint signal

t\inT

when the state of the world is

\theta\in\Theta

.

By joining those two definitions, one can define

\Gamma=(G,S)

as an incomplete information game.[2] A decision rule for the incomplete information game

\Gamma=(G,S)

is a mapping

\sigma:T x \Theta\Delta(A)

. Intuitively, the value of decision rule

\sigma(a|t,\theta)

can be thought of as a joint recommendation for players to play the joint mixed strategy

\sigma(a)\in\Delta(A)

when the joint signal received is

t\inT

and the state of the world is

\theta\in\Theta

.

Definition

A Bayes correlated equilibrium (BCE) is defined to be a decision rule

\sigma

which is obedient: that is, one where no player has an incentive to unilaterally deviate from the recommended joint strategy, for any possible type they may be. Formally, decision rule

\sigma

is obedient (and a Bayes correlated equilibrium) for game

\Gamma=(G,S)

if, for every player

i\inI

, every signal

ti\inTi

and every action

ai\inAi

, we have
\sum
a-i,t-i,\theta

\psi(\theta)\pi(ti,t-i|\theta)\sigma(ai,a-i|ti,t-i,\theta)ui(ai,a-i,\theta)

\geq

\sum
a-i,t-i,\theta

\psi(\theta)\pi(ti,t-i|\theta)\sigma(ai,a-i|ti,t-i,\theta)ui(a'i,a-i,\theta)

for all

a'i\inAi

.

That is, every player obtains a higher expected payoff by following the recommendation from the decision rule than by deviating to any other possible action.

Relation to other concepts

Bayesian Nash equilibrium

Every Bayesian Nash equilibrium (BNE) of an incomplete information game can be thought of a as BCE, where the recommended joint strategy is simply the equilibrium joint strategy.

Formally, let

\Gamma=(G,S)

be an incomplete information game, and let

s:T\Delta(A)

be an equilibrium joint strategy, with each player

i

playing

si(ai|ti)\in\Delta(Ai)

. Therefore, the definition of BNE implies that, for every

i\inI

,

ti\inTi

and

ai\inAi

such that

si(ai|ti)>0

, we have
\sum
a-i,t-i,\theta

\psi(\theta)\pi(ti,t-i|\theta)\left(\prodjsj(aj|tj)\right)ui(ai,a-i,\theta)

\geq

\sum
a-i,t-i,\theta

\psi(\theta)\pi(ti,t-i|\theta)\left(\prodjsj(aj|tj)\right)ui(a'i,a-i,\theta)

for every

a'i\inAi

.

If we define the decision rule

\sigma

on

\Gamma

as

\sigma(a|t,\theta)=s(a|t)=\prodisi(ai|ti)

for all

t\inT

and

\theta\in\Theta

, we directly get a BCE.

Correlated equilibrium

If there is no uncertainty about the state of the world (e.g., if

\Theta

is a singleton), then the definition collapses to Aumann's correlated equilibrium solution.[3] In this case,

\sigma\in\Delta(A)

is a BCE if, for every

i\inI

, we have[4]
\sum
a-i\inA{-i
} \sigma (a_i, a_) u_i(a_i, a_) \geq \sum_ \sigma (a_i, a_) u_i(a'_i, a_)

for every

a'i\inAi

, which is equivalent to the definition of a correlated equilibrium for such a setting.

Bayesian persuasion

Additionally, the problem of designing a BCE can be thought of as a multi-player generalization of the Bayesian persuasion problem from Emir Kamenica and Matthew Gentzkow.[5] More specifically, let

v:A x \ThetaR

be the information designer's objective function. Then her ex-ante expected utility from a BCE decision rule

\sigma

is given by:

V(\sigma)=\suma,\psi(\theta)\pi(t|\theta)\sigma(a|t,\theta)v(a,\theta)

If the set of players

I

is a singleton, then choosing an information structure to maximize

V(\sigma)

is equivalent to a Bayesian persuasion problem, where the information designer is called a Sender and the player is called a Receiver.

Notes and References

  1. Bergemann . Dirk . Morris . Stephen . Bayes correlated equilibrium and the comparison of information structures in games . Theoretical Economics . 2016 . 11 . 2 . 487-522 . 10.3982/TE1808 . 10419/150284 . free .
  2. Gossner . Olivier . Comparison of Information Structures . Games and Economic Behavior . 2000 . 30 . 1 . 44-63 . 10.1006/game.1998.0706 . 10230/596 . free .
  3. Aumann . Robert J. . Robert J. Aumann . Correlated Equilibrium as an Expression of Bayesian Rationality . Econometrica . 1987 . 55 . 1 . 1-18 . 10.2307/1911154 .
  4. Bergemann . Dirk . Morris . Stephen . Dirk Bergemann . Stephen Morris (economist) . Information Design: A Unified Perspective . Journal of Economic Literature . 2019 . 57 . 1 . 44-95 . 10.1257/jel.20181489 .
  5. Kamenica . Emir . Gentzkow . Matthew . 2011-10-01 . Bayesian Persuasion . American Economic Review . en . 101 . 6 . 2590–2615 . 10.1257/aer.101.6.2590 . 0002-8282.