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Nash Equilibrium Refinements

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Game Theory BasicsNash Equilibrium→→Nash Refinements: Trembling Hand PerfectionStrategic Form Games and Nash Equilibrium
game-theory equilibrium solution-concepts

Core Idea

A Nash equilibrium refinement is a criterion that eliminates some Nash equilibria as implausible or unreasonable. Common refinements include subgame perfection (no implausible threats), perfect Bayesian equilibrium (consistent beliefs), and trembling-hand perfection (robustness to small errors). Refinements help pin down equilibrium when multiple equilibria exist.

How It's Best Learned

Start with games that have multiple Nash equilibria. Show why some seem unreasonable. Apply different refinements and see which equilibria survive.

Explainer

From Nash equilibrium, you know that an equilibrium is a strategy profile where no player can improve their payoff by unilaterally changing their strategy. This is a powerful concept, but in many games it is too permissive — it admits equilibria that rely on threats no rational player would actually carry out, beliefs that make no sense given the structure of the game, or strategies that would collapse if players made even tiny mistakes. Equilibrium refinements are additional criteria designed to eliminate these implausible equilibria and sharpen predictions.

The most intuitive refinement is subgame perfection, which requires that strategies form a Nash equilibrium in every subgame of the original game — not just the game as a whole. Consider a market entry game: an incumbent threatens to start a price war if a challenger enters. This threat can sustain an equilibrium where the challenger stays out. But if entry actually occurred, would the incumbent really follow through? A price war hurts the incumbent too. Subgame perfection requires that the incumbent's strategy be optimal even after entry happens, eliminating threats that are not credible when actually tested. You find subgame perfect equilibria through backward induction: start at the end of the game tree, determine optimal play at each final decision node, then work backwards. Any equilibrium that relies on a player acting against their own interest at some decision point is eliminated.

When games involve incomplete information — players are uncertain about others' types or previous actions — subgame perfection alone is insufficient because there may not be well-defined subgames to analyze. Perfect Bayesian equilibrium (PBE) addresses this by requiring two things simultaneously: strategies must be sequentially rational (optimal at every information set given beliefs), and beliefs must be consistent with strategies via Bayes' rule wherever possible. In a signaling game, for example, PBE pins down not just what the sender does and how the receiver responds, but what the receiver believes about the sender's type after observing each possible signal. Beliefs cannot be arbitrary — they must be updated rationally from prior probabilities and observed actions.

A third important refinement is trembling-hand perfection, which tests whether an equilibrium is robust to small mistakes. The idea is that every player has a tiny probability of "trembling" and playing any available strategy by accident. A trembling-hand perfect equilibrium must remain a best response even when opponents occasionally err. This eliminates equilibria that survive only because a player is indifferent between strategies and the equilibrium relies on them choosing a specific one that would be suboptimal if opponents ever deviated. Each refinement captures a different aspect of strategic plausibility — credibility of threats (subgame perfection), consistency of beliefs (PBE), or robustness to errors (trembling hand) — and the choice of which refinement to apply depends on the game's structure and what kind of implausibility you are trying to rule out.

Practice Questions 5 questions

Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Dividing Integers → Unit Rates → Proportions → Percent Concept → Converting Between Fractions, Decimals, and Percents → Operations with Rational Numbers → Two-Step Equations → Solving Multi-Step Equations → Equations with Variables on Both Sides → Literal Equations → Slope-Intercept Form → Point-Slope Form → Writing Linear Equations → Parallel and Perpendicular Line Slopes → Graphing Linear Equations → Piecewise Functions → One-Sided Limits → Continuity Definition → Limits and Continuity in Multiple Variables → Functions of Several Variables → Continuity in Multiple Variables → Partial Derivatives: Definition and Computation → Differentiability in Multiple Variables → Differentiability in Multivariable Functions → Total Differential and Linear Approximation → Chain Rule for Multivariable Functions → Implicit Differentiation → Related Rates → Optimization Problems → Critical Points of Multivariable Functions → Critical Points and Classification of Extrema → Second Partial Test for Local Extrema (Hessian) → The Hessian Matrix and Second Derivative Test → Unconstrained Optimization: Finding Extrema → Optimization in Multiple Variables → Lagrange Multipliers → Constrained Optimization and Lagrange Multipliers → Utility and Preferences → Marginal Utility and Diminishing Returns → Profit Maximization → Perfect Competition → Shutdown and Breakeven Decisions → Monopoly → Monopolistic Competition → Oligopoly and Strategic Behavior → Game Theory Basics → Nash Equilibrium → Nash Equilibrium Refinements

Longest path: 100 steps · 522 total prerequisite topics

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