EscapeCapitalism: AI-Driven Economic Simulation
Technical Requirements and Implementation Specification
1. System Overview
Core Simulation Engine
- Implementation using Python 3.11 with NumPy and SciPy for mathematical computations
- Event-driven architecture using RxPY for reactive programming
- Discrete event simulation framework built on SimPy 4.0
- Core loop running at 60 ticks per second with configurable time dilation
- State management using Redis for real-time data and PostgreSQL for persistence
AI Agent Architecture
- Multi-agent system built on Ray RLlib framework
- Proximal Policy Optimization (PPO) as primary reinforcement learning algorithm
- Agent hierarchy: Corporation Agents, Market Makers, Regulatory Agents
- Neural network architecture:
- Input layer: 256 neurons (market state vectors)
- Hidden layers: 512-256-128 neurons with ReLU activation
- Output layer: Action space using softmax activation
- Experience replay buffer size: 1M samples
- Batch size: 256 samples
Evolutionary Mechanisms
- Genetic Algorithm implementation using DEAP framework
- Population size: 100 agents per generation
- Selection method: Tournament selection (size 3)
- Crossover rate: 0.8
- Mutation rate: 0.1
- Generation interval: Every 10,000 simulation ticks
Economic Metrics
- Real-time metrics:
- GDP calculation interval: 1000 ticks
- Market liquidity index
- Price stability indicators
- Wealth distribution (Gini coefficient)
- Success criteria:
- System stability: < 5% market crashes per year
- Agent profitability: > 60% agents maintaining positive growth
- Market efficiency: < 2% arbitrage opportunities
2. Technical Architecture
Backend System
- Core server: FastAPI on Uvicorn
- Databases:
- PostgreSQL 15 for persistent storage
- Redis 7.0 for real-time state
- ClickHouse for analytics
- Message broker: RabbitMQ for event distribution
- Processing engine: Apache Spark for batch analytics
API Specifications
- REST API for game state management
- WebSocket for real-time updates
- GraphQL for complex queries
- Rate limiting: 1000 requests/minute per client
- Response time SLA: 50ms (95th percentile)
AI Model Specifications
- Training infrastructure:
- GPU requirements: NVIDIA A100 or equivalent
- Distributed training using Ray
- Model checkpointing every 1000 episodes
- Datasets:
- Historical market data: 10 years minimum
- Corporate financial statements
- Economic indicators
- Minimum dataset size: 1TB
Integration Architecture
- Microservices communication via gRPC
- Event sourcing using Apache Kafka
- Service mesh: Istio
- API gateway: Kong
- Load balancing: HAProxy
3. Simulation Parameters
Economic Variables
- Currency system:
- Base currency precision: 8 decimal places
- Multiple currency support (up to 10)
- Exchange rate dynamics
- Resources:
- Raw materials: 50 types
- Manufactured goods: 200 types
- Services: 100 types
- Asset classes:
- Stocks
- Bonds
- Real estate
- Commodities
- Derivatives
Market Dynamics
- Price discovery:
- Order book depth: 1000 levels
- Price tick size: 0.0001
- Matching engine throughput: 100k orders/second
- Supply/Demand:
- Dynamic elasticity calculations
- Supply chain latency simulation
- Inventory management systems
Agent Behavior Parameters
- Decision intervals: 1-1000 ticks
- Risk models:
- Value at Risk (VaR) calculation
- Monte Carlo simulation
- Black-Scholes option pricing
- Investment strategies:
- Long-term growth
- High-frequency trading
- Value investing
- Momentum trading
4. Game Mechanics
Simulation Rules
- Real-time simulation with configurable time dilation
- Tick rate: 60 Hz base speed
- Time compression: 1x to 10000x
- Transaction validation latency: < 100ms
Agent Interactions
- Direct trading
- Contract formation
- Resource competition
- Market manipulation detection
- Coalition formation
Resource Systems
- Resource discovery rate
- Production efficiency factors
- Transportation costs
- Storage limitations
Success Metrics
- Corporate valuation
- Market share
- Innovation index
- Sustainability score
- Social impact rating
5. Implementation Timeline
Phase 1: Core Engine (3 months)
- Week 1-4: Basic simulation engine
- Week 5-8: Database implementation
- Week 9-12: API development
Phase 2: AI Development (4 months)
- Week 1-4: Agent architecture
- Week 5-8: Training pipeline
- Week 9-16: Model training and optimization
Phase 3: Market Mechanics (3 months)
- Week 1-6: Trading systems
- Week 7-12: Economic simulation
Phase 4: Integration (2 months)
- Week 1-4: System integration
- Week 5-8: Testing and optimization
6. Technical Constraints
Performance Requirements
- Maximum latency: 50ms
- Minimum throughput: 100k transactions/second
- CPU utilization: < 80%
- Memory usage: < 64GB per server
- Network bandwidth: 10Gbps minimum
Scalability
- Horizontal scaling up to 1000 nodes
- Vertical scaling up to 64 cores per node
- Database sharding threshold: 1TB per shard
- Load balancing capacity: 1M concurrent users
Security
- End-to-end encryption
- Multi-factor authentication
- Rate limiting
- DDoS protection
- Regular security audits
Storage Limitations
- Maximum database size: 100TB
- Time series data retention: 5 years
- Backup frequency: Daily
- Archive policy: Monthly consolidation
7. Monitoring and Analysis
Data Collection
- Metrics collection interval: 1 second
- Log aggregation using ELK stack
- Distributed tracing using Jaeger
- Performance profiling using cProfile
Performance Metrics
- System metrics:
- CPU, memory, disk usage
- Network latency and throughput
- Database query performance
- Business metrics:
- Transaction volume
- Market liquidity
- Agent performance
- Economic indicators
Visualization Requirements
- Real-time dashboards using Grafana
- Economic visualizations using D3.js
- Market analysis tools using Plotly
- Custom visualization engine for complex economic relationships
Logging System
- Log levels: DEBUG, INFO, WARN, ERROR, FATAL
- Log rotation: Daily
- Retention period: 90 days
- Structured logging format: JSON
- Centralized log management using Logstash