nash-game-theory

What problem does it solve?

It helps teams design, implement, and test new game-theory environments in NASH so that they can be validated against Nash-equilibrium convergence behavior rather than relying on ad-hoc implementations.

Core Features & Use Cases

• Environment design blueprint: Provides a step-by-step template for creating a configurable game environment in src/environments/ with clear validation hooks.
• Research-to-implementation workflow: Guides parallel subagent research to derive payoff structures, theoretical predictions, and parameter ranges before coding.
• CLI integration readiness: Focuses on wiring the new environment into NASH’s run/validate/viz workflow through the expected environment class and factory pattern.
• Use Case: When no existing model matches your incentive structure (e.g., coordination incentives, signaling-like mechanisms, custom policy games), use this skill to formalize it as a reusable environment and generate convergence/benchmark tests.

Quick Start

Ask an AI to help you create a new NASH environment file and its corresponding test by following the nash-game-theory template, using your game name, payoff matrix, and expected equilibrium prediction.