A comprehensive, modular framework for advanced game theory applications, integrating classical, computational, behavioral, and AI-driven game theory into a unified, scalable ecosystem.
GameForge is a powerful, extensible library designed to facilitate game theory research, simulation, and application across diverse domains. Whether you are a researcher, developer, or strategist, GameForge provides tools to model, analyze, and refine strategic interactions using a wide range of theoretical and computational approaches.
GameForge supports:
- Classical Game Theory – Nash equilibria, mixed strategies, extensive and strategic forms.
- Computational Game Theory – Algorithmic analysis, AI-driven game strategies, and equilibrium computation.
- Behavioral Game Theory – Human decision-making biases, prospect theory, and bounded rationality.
- Multi-Agent Systems & AI – Reinforcement learning, adaptive strategies, and agent-based modeling.
- Metagaming & Dynamic Environments – Iterated play, evolving incentives, and external factors.
- Extensive Form (game trees, sequential decisions, perfect/imperfect information)
- Strategic Form (payoff matrices, simultaneous play)
- Graph-Based & Hybrid Representations
- Equilibrium Computation (Nash, correlated, evolutionary)
- Algorithmic Strategy Optimization
- Monte Carlo & Minimax Simulations
- Prospect Theory & Risk Preferences
- Social Preferences & Fairness
- Framing Effects & Decision Heuristics
- Reinforcement Learning Agents
- Adaptive Strategy Learning
- Agent-Based Modeling for Dynamic Environments
- Finite vs. Infinite Horizon Adjustments
- Endgame Scenarios & Tipping Points
- Computational Complexity & Algorithmic Playability
- Iterated Play & Reputation Systems
- Policy & Regulatory Game Theory
- Real-World Incentive Modeling
GameForge is in active development. Once released, it will be available via PyPI:
pip install gameforge