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Extensive Form Games and Game Trees

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Game Theory BasicsStrategic Form Games and Nash EquilibriumSubgame Perfect Equilibrium
game-theory sequential-games information

Core Idea

Extensive form games represent sequential decision-making using game trees with nodes (decision points), edges (actions), and information sets (showing what players know). This representation captures move order and information asymmetries absent from strategic form. Perfect information games have singleton information sets; incomplete information is modeled through nature's moves.

Explainer

The strategic (normal) form you already know represents games as a matrix of strategies and payoffs. This works well when players move simultaneously, but it suppresses a crucial feature of many real interactions: timing. When a firm observes a rival's price before choosing its own, or when a chess player sees the opponent's move before responding, the sequence of decisions matters enormously. The extensive form captures this by representing the game as a tree — a branching structure where each node is a decision point, each branch is an available action, and the terminal nodes carry payoffs.

Reading a game tree is straightforward. The tree starts at an initial node, typically drawn at the top or left. At each decision node, the label identifies which player moves, and the branches represent that player's available actions. Following any complete path from the root to a terminal node yields a specific outcome with payoffs for all players. The critical addition beyond the strategic form is the information set — a collection of decision nodes where a player cannot distinguish which node they are at. If player 2 moves after player 1 but does not observe player 1's action, player 2's decision nodes are grouped into a single information set, drawn as a dashed oval connecting them. The player must choose the same action at all nodes within an information set, since they cannot tell the nodes apart.

This structure creates a precise taxonomy. In a game of perfect information, every information set contains exactly one node — each player always knows exactly where they are in the tree. Chess, tic-tac-toe, and ultimatum bargaining are perfect-information games. In a game of imperfect information, at least one information set contains multiple nodes — some player moves without knowing a prior action. Poker is the classic example: you do not see the other player's cards. Incomplete information — where players are uncertain about each other's payoffs or types — is modeled by adding an initial move by Nature, a fictitious player who randomly selects types according to known probabilities. A firm unsure whether its rival has high or low costs is playing a game where Nature chose the rival's cost type.

The extensive form matters because it refines the set of equilibria. In the strategic form, a player can threaten any action, and Nash equilibrium only requires that threats not be tested. But in the extensive form, we can ask whether a threat is credible — whether a player would actually follow through if the relevant decision node were reached. This leads directly to refinements like subgame-perfect equilibrium, solved by backward induction: start at the terminal nodes, determine what the last mover would do, fold that back to the previous mover's decision, and work backward to the root. Strategies that rely on incredible threats — "I'll start a price war that destroys us both" when the threatening firm would never actually do so — are eliminated. The extensive form thus gives game theory its ability to analyze commitment, credibility, and the strategic value of moving first or last.

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 Trees

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