Metadata-Version: 2.2
Name: gameforged
Version: 0.1.1
Summary: A comprehensive Python framework for game theory modeling, analysis, and simulation.
Author-email: DirtyWork Solutions Limited <gameforge@open.dirtywork.solutions>
Project-URL: Homepage, https://open.dirtywork.solutions/gameforgde
Project-URL: Documentation, https://open.dirtywork.solutions/gameforged/docs
Project-URL: Source, https://github.com/DirtyWork-Solutions/GameForge
Project-URL: Issues, https://github.com/DirtyWork-Solutions/GameForge/tree/main/.github/ISSUE_TEMPLATE
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cachetools
Requires-Dist: pydantic
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pyutile

# **GameForge**  

*A comprehensive, modular framework for advanced game theory applications, integrating classical, computational, behavioral, and AI-driven game theory into a unified, scalable ecosystem.*  

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## **Overview**  

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.  

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## **Features**  

### **1. Game Representation & Modeling**  
- **Extensive Form** (game trees, sequential decisions, perfect/imperfect information)  
- **Strategic Form** (payoff matrices, simultaneous play)  
- **Graph-Based & Hybrid Representations**  

### **2. Computational Analysis**  
- **Equilibrium Computation** (Nash, correlated, evolutionary)  
- **Algorithmic Strategy Optimization**  
- **Monte Carlo & Minimax Simulations**  

### **3. Behavioral & Psychological Modeling**  
- **Prospect Theory & Risk Preferences**  
- **Social Preferences & Fairness**  
- **Framing Effects & Decision Heuristics**  

### **4. Multi-Agent Systems & AI Integration**  
- **Reinforcement Learning Agents**  
- **Adaptive Strategy Learning**  
- **Agent-Based Modeling for Dynamic Environments**  

### **5. Game Length & Complexity Refinements**  
- **Finite vs. Infinite Horizon Adjustments**  
- **Endgame Scenarios & Tipping Points**  
- **Computational Complexity & Algorithmic Playability**  

### **6. Metagaming & External Incentives**  
- **Iterated Play & Reputation Systems**  
- **Policy & Regulatory Game Theory**  
- **Real-World Incentive Modeling**  

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## **Installation**  

GameForge is in active development. Once released, it will be available via PyPI:  

```bash
pip install gameforge
