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