🎲 Monte Carlo Monopoly Strategy Engine

A high-performance C++ backend designed to simulate millions of Monopoly games to derive optimal strategic metrics, ROI curves, and landing probabilities.

🚀 Overview

Monte Carlo Monopoly is more than just a game simulation; it's a strategic analysis tool. By utilizing Monte Carlo methods, the engine executes thousands of iterations to model the complex economics of Monopoly. It provides the data necessary to drive a real-time analytics dashboard, helping players understand which properties offer the best yield under varying risk tolerances.

Key Features

• Monte Carlo Simulation Engine: Run thousands of games in seconds to capture statistical significance.
• Lead Strategist (AGENT_01): A sophisticated AI agent with a parameterized risk tolerance (alpha).
• Dynamic ROI Analytics: Calculates Realized Return on Investment for every property group.
• Board Heatmapping: Statistical landing probabilities across all 40 squares.
• JSON API Output: Structured payload designed for direct integration with React-based frontend dashboards.
• Rule Customization: Toggle specialized rules like Free Parking Windfalls, Rapid Auctions, and Mortgage Leverage.

🛠️ Performance & Strategy

The engine utilizes a specialized Decision Tree logic for agents. The alpha parameter (0.0 to 1.0) dictates an agent's risk appetite:

• Low Alpha (Conservative): Maintains higher cash reserves, avoids aggressive property builds.
• High Alpha (Aggressive): Reinvests liquidity immediately into houses/hotels to maximize rent yields.

💻 CLI Documentation

The executable supports a variety of flags to customize the simulation environment:

Flag	Description	Default
--sims <int>	Number of game iterations to run	1
--agents <int>	Number of AI agents (2-8)	4
--liquidity <int>	Starting cash for all agents	1500
--alpha <float>	Risk tolerance for AGENT_01 (0.0 to 1.0)	0.5
--json	Enable structured JSON output mode	false
--free-parking-windfall	Enables cash collection on Free Parking	false
--stochastic-rent	Adds variance to rent payments	false

📦 Building & Execution

Prerequisites

• CMake (3.15+)
• C++17 Compiler (GCC, Clang, or MSVC)

Instructions

1. Clone the repository:

   git clone https://github.com/godking123/Monte-Carlo-Monopoly.git

2. Build the project:

   mkdir build && cd build
   cmake ..
   cmake --build . --config Release

3. Run the Simulation:

   ./MonteCarlo-Monopoly --sims 1000 --json --alpha 0.8

📊 JSON API Structure

When running with the --json flag, the engine emits a payload containing:

• boardHeatmap: Frequency of landings for all 40 positions.
• propertyEfficiency: Real-time ROI and landing probability per property.
• strategyEngine: Winning probabilities, ROI curves (Aggressive vs. Conservative), and optimal property weightings.
• propertyMatrix: High-fidelity data for the dashboard (Live logs, agent net worth, and property ownership).

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Developed for advanced game theory analysis and statistical modeling.