What is Sim Mcp Token

sim-mcp-token is an agent-based economic simulation that explores resource allocation, pricing dynamics, and wealth distribution among autonomous agents. It incorporates various economic policies such as taxes and includes features for parameter experimentation and analysis.

Use cases

Use cases include simulating the effects of different tax policies on wealth distribution, analyzing resource allocation strategies under varying conditions, and experimenting with economic parameters like price elasticity and regeneration rates.

How to use

To use sim-mcp-token, ensure you have Python 3.7+ and NumPy installed. You can run the full experimentation suite by executing the command: python main.py.

Key features

Key features include 50 autonomous agents with evolving resource needs, a resource ecosystem with price elasticity and regeneration patterns, dynamic pricing based on supply and demand, a tax system for wealth redistribution, bankruptcy detection, and analysis tools such as Gini coefficient calculation.

Where to use

sim-mcp-token can be used in fields such as economics, social sciences, and educational simulations to study resource management, economic policies, and agent behavior in a controlled environment.

Agent-Based Economic Simulation

A dynamic agent-based simulation modeling economic interactions between autonomous agents and limited resources. The system explores resource allocation strategies, price dynamics, and wealth distribution under various economic parameters.

Key Features

• Autonomous Agents:
  50 agents with evolving resource needs, income/expense dynamics, and bankruptcy rules

• Resource Ecosystem:
  3 resources with price elasticity, regeneration patterns, and capacity adaptation

• Economic Mechanics:

  • Dynamic pricing based on supply/demand
  • Tax system with wealth redistribution
  • Bankruptcy detection and agent removal
  • Resource capacity adaptation to economic output

• Analysis Tools:

  • Gini coefficient calculation
  • Parameter experimentation framework
  • Key metric tracking (bankruptcies, balances, prices)

Getting Started

Requirements

• Python 3.7+
• NumPy

Basic Usage

Run full experimentation suite:

python main.py

Code Structure

Core Parameters (constants.py)

Experimentation Insights

The system tests four key parameters across ranges:

1. Price Elasticity (0.01-0.1)
2. Resource Regeneration (0.005-0.02)
3. Tax Rates (0-5%)
4. Expense Rates (10-50%)

Sample findings:

Tax rate that minimizes bankruptcies: 0.0278
Regen rate that maximizes average final balance: 0.0189

Key Metrics Tracked

• Average agent balance
• Wealth inequality (Gini coefficient)
• Bankruptcy count
• Resource price stability
• System-wide economic output

Simulation Flow

1. Price updates based on resource utilization
2. Resource allocation through agent bidding
3. Income distribution and expense deduction
4. Tax collection/redistribution
5. Bankruptcy checks and agent removal
6. Resource regeneration and capacity adjustment
7. Agent behavior adaptation

Analysis Capabilities

• Parameter sensitivity testing
• Optimal policy identification
• System stability evaluation
• Emergent pattern detection
• Equilibrium state analysis