This is a simulation game to illustrate the Sub Game Perfect Nash Equilibrium concept in game theory:
This is an interactive simulation game designed to illustrate the Subgame Perfect Nash Equilibrium (SPNE) concept and Backward Induction in Game Theory.
📖 What is the Centipede Game?
The Centipede Game is a famous extensive-form dynamic game in game theory first introduced by economist Robert Aumann in 1981. Two players take turns deciding whether to "Take" (end the game and secure the larger share of the current pot) or "Pass" (pass the turn to the other player, causing the total pot to grow).
Key Concepts Demonstrated:
- Subgame Perfect Nash Equilibrium (SPNE): A strategic equilibrium refinement where every player plays a Nash equilibrium in every subgame of the overall game.
- Backward Induction: The analytical process of solving dynamic games by starting at the final decision node and working backward to determine optimal choices at each stage.
- The Paradox of Backward Induction: Game theory dictates that rational players using backward induction will choose to Take on the very first move (resulting in the lowest overall payoff). However, experimental evidence shows human players frequently choose to Pass multiple times to build up higher collective rewards.
♟️ Centipede Game: SPNE & Backward Induction
Explore Sequential Dynamic Games, Subgames, and the Paradox of Backward Induction
Node 1: Choose to Take (end game & take majority pot) or Pass (grow pot & pass turn).
🧠 Backward Induction Solver
Subgame Perfect Nash Equilibrium (SPNE) is found by solving the game backward from the terminal nodes.
Game History
| Rnd | End Node | Action | Payoffs (P1, P2) |
|---|
♟️ Centipede Game: SPNE & Backward Induction
Explore Sequential Dynamic Games, Subgames, and the Paradox of Backward Induction
Node 1: Choose to Take (end game & take majority pot) or Pass (grow pot & pass turn).
🧠 Backward Induction Solver
Subgame Perfect Nash Equilibrium (SPNE) is found by solving the game backward from the terminal nodes.
Game History
| Rnd | End Node | Action | Payoffs (P1, P2) |
|---|