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Signaling Games: Separating and Pooling Equilibria

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Bayesian Games (Games of Incomplete Information)Adverse Selection and Screening Mechanisms+2 moreLabor Market Signaling: Education as a Signal
game-theory information-asymmetry

Core Idea

In signaling games, the informed player moves first with an action (signal) that communicates private information. Separating equilibria have different types taking different actions; pooling equilibria have all types taking the same action. The Intuitive Criterion eliminates equilibria depending on unreasonable out-of-equilibrium beliefs, selecting among multiple equilibria.

Explainer

From your study of adverse selection and screening, you know that information asymmetry creates problems: when one side of a market knows more than the other, uninformed parties cannot distinguish good types from bad. In screening models, the uninformed party moves first, designing a menu of contracts to sort the informed party. Signaling games flip this structure — here the informed party moves first, taking a costly action that conveys information about their type. The classic example is Spence's job market signaling: a worker knows their own productivity, and they choose how much education to get before an employer makes a wage offer. Education may or may not improve productivity directly — what matters is that it is differentially costly across types, allowing it to function as a credible signal.

A separating equilibrium is one where different types choose different signals, fully revealing their private information. In the education example, high-ability workers get a degree while low-ability workers do not. This works when the signal is costly enough that low types would not want to mimic high types, even for the higher wage. The condition is called the single-crossing property: the marginal cost of the signal must differ across types so that indifference curves of different types cross only once in signal-wage space. High-ability workers find education less costly (in effort, time, or difficulty), so they are willing to invest in a level of education that low-ability workers would find prohibitively expensive. The employer, observing the education level, correctly infers the worker's type and pays accordingly.

A pooling equilibrium is one where all types choose the same signal, so no information is transmitted. If all workers get the same education level regardless of ability, the employer learns nothing and pays the average-productivity wage. Pooling equilibria can be sustained when the signal cost is high enough that no type finds it worthwhile to deviate — or when beliefs about deviators are pessimistic enough that deviating is not rewarded. This multiplicity of equilibria is a characteristic feature of signaling games: for many parameter values, both separating and pooling equilibria exist, along with partial-pooling equilibria where some types separate and others pool.

The Intuitive Criterion, introduced by Cho and Kreps, is the standard refinement for selecting among these equilibria. The idea is to eliminate equilibria that are sustained only by unreasonable beliefs about off-equilibrium actions. If a particular deviation from the equilibrium strategy could only conceivably benefit a high type (because a low type would lose money even with the most optimistic belief about the employer's response), then the employer should infer that any deviator is a high type. Formally, if a signal is equilibrium dominated for one type — meaning that type would never benefit from sending it regardless of how the receiver responds — then the receiver should not attribute that signal to that type. Applying this criterion typically eliminates pooling equilibria and many partial-pooling equilibria, selecting the most efficient separating equilibrium as the unique prediction. This refinement is widely applied in industrial organization, finance (firms signaling quality through dividends or debt), and political economy (candidates signaling competence through policy positions).

Practice Questions 5 questions

Prerequisite Chain

Understanding ZeroThe Number ZeroCounting to FiveCounting to 10Counting to 20Counting a Set of Objects Up to 20Cardinality: The Last Number CountedMatching Numerals to QuantitiesSubitizing Small QuantitiesAddition Within 10Number Bonds to 10Addition Within 20Doubles and Near DoublesDoubles Facts Within 10Near Doubles Facts Within 20Mental Math Strategies for AdditionMental Math: Adding and Subtracting TensAddition Within 100Repeated Addition as MultiplicationMultiplication as Equal GroupsMultiplication: ArraysBasic Multiplication Facts (0s, 1s, 2s, 5s, 10s)Multiplication Facts Within 100Division as Equal SharingDivision as Grouping (Measurement Division)Division: Grouping (Repeated Subtraction) ModelDivision: Fair Sharing ModelDivision as Equal SharingDivision as GroupingBasic Division FactsDivision Facts Within 100Multiplication and Division Fact FamiliesRelationship Between Multiplication and DivisionDivision Facts as Inverse of MultiplicationRemainders and Quotients in DivisionDivision Word ProblemsMulti-Step Word ProblemsSolving Multi-Step Word ProblemsMultiplication Word ProblemsDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueIntegers and the Number LineComparing and Ordering IntegersAbsolute ValueAdding IntegersSubtracting IntegersMultiplying IntegersDividing IntegersUnit RatesProportionsPercent ConceptConverting Between Fractions, Decimals, and PercentsOperations with Rational NumbersTwo-Step EquationsSolving Multi-Step EquationsEquations with Variables on Both SidesLiteral EquationsSlope-Intercept FormPoint-Slope FormWriting Linear EquationsParallel and Perpendicular Line SlopesGraphing Linear EquationsPiecewise FunctionsOne-Sided LimitsContinuity DefinitionLimits and Continuity in Multiple VariablesFunctions of Several VariablesContinuity in Multiple VariablesPartial Derivatives: Definition and ComputationDifferentiability in Multiple VariablesDifferentiability in Multivariable FunctionsTotal Differential and Linear ApproximationChain Rule for Multivariable FunctionsImplicit DifferentiationRelated RatesOptimization ProblemsCritical Points of Multivariable FunctionsCritical Points and Classification of ExtremaSecond Partial Test for Local Extrema (Hessian)The Hessian Matrix and Second Derivative TestUnconstrained Optimization: Finding ExtremaOptimization in Multiple VariablesLagrange MultipliersConstrained Optimization and Lagrange MultipliersUtility and PreferencesMarginal Utility and Diminishing ReturnsProfit MaximizationPerfect CompetitionShutdown and Breakeven DecisionsMonopolyMonopolistic CompetitionOligopoly and Strategic BehaviorGame Theory BasicsNash EquilibriumNash Equilibrium RefinementsStrategic Form Games and Nash EquilibriumExtensive Form Games and Game TreesSubgame Perfect EquilibriumPerfect Bayesian EquilibriumPooling and Separating EquilibriaAdverse Selection and Screening MechanismsInsurance Markets with Adverse SelectionAdverse SelectionSignaling GamesSignaling Games: Separating and Pooling Equilibria

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