A topic in the Open Knowledge Graph — a free, open map of 15,290 topics and the order to learn them in.

Game Theory Basics

College Depth 97 in the knowledge graph I know this Set as goal
189topics build on this
520prerequisites beneath it
See this on the map →
Scarcity and Opportunity CostExpected Value+1 moreAdverse Selection and Screening MechanismsAdverse Selection and Signaling+14 more
game theory normal form payoff matrix dominant strategy prisoners dilemma

Core Idea

Game theory studies strategic interactions where each player's payoff depends on the choices of all players. A game in normal form specifies players, strategies, and payoffs in a matrix. A dominant strategy is one that is optimal regardless of what the opponent does. The Prisoner's Dilemma is the canonical example where individual dominant strategies lead to a mutually inferior outcome, illustrating why coordination problems and market failures arise even among rational agents.

How It's Best Learned

Work through the Prisoner's Dilemma payoff matrix by hand, identifying dominant strategies before defining Nash equilibrium. Then apply the framework to advertising decisions, pricing, and arms-race scenarios.

Common Misconceptions

Explainer

Game theory studies decision-making in strategic interactions — situations where your payoff depends not just on your own choice but on the choices of others. This distinguishes it from the optimization problems you've seen elsewhere in microeconomics, where you simply maximize utility or profit given fixed prices and constraints. In strategic situations, your best action depends on what others do, and their best action depends on what you do. Game theory provides the tools to analyze this mutual dependence precisely.

Every game in normal form has three elements: players, strategies, and payoffs. A payoff matrix displays this information visually. Each row is a strategy for player 1, each column is a strategy for player 2, and each cell shows what both players earn for that combination of choices. Reading the matrix is itself a skill — by convention, the row player's payoff is listed first. Before solving any game, spend a moment mapping out what each cell means in terms of the actual situation being modeled.

A dominant strategy is one that is optimal regardless of what the opponent does. To identify it: compare each strategy of player 1 across all columns. If one row always gives a payoff at least as high as every other row, that row dominates. When a dominant strategy exists, a rational player should always choose it — no prediction about the opponent is needed. This makes dominant-strategy reasoning especially robust. Most games, however, do not have dominant strategies for all players, which is why Nash equilibrium (a concept you'll study next) is the more general solution concept.

The Prisoner's Dilemma is the most important example in introductory game theory precisely because it exposes the limits of individual rationality. Each player has a dominant strategy — defect. Both play it. But the result (mutual defection) is worse for both than if they had cooperated. The core insight: individual rationality does not guarantee collective optimality. You've already seen this idea in the context of market failures and public goods: the rational choice for each individual (free-ride, pollute, defect) undermines the outcome for everyone. The Prisoner's Dilemma gives that intuition mathematical precision.

This framework applies far beyond stylized examples. Firms deciding whether to advertise, countries choosing military spending levels, and commuters choosing routes all face Prisoner's Dilemma-style structures. In each case, the temptation to defect is individually rational but collectively costly. Understanding this structure tells you when regulation, contracts, or repeated interaction might enable better outcomes — because when the game is played repeatedly, the calculus changes and cooperation can become self-sustaining. That extension is where the analysis gets richer.

Practice Questions 3 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 Basics

Longest path: 98 steps · 520 total prerequisite topics

Prerequisites (3)

Leads To (16)