Papers › OmniEcon Nexus: Global Microeconomic Simulation Engine
OmniEcon Nexus: Global Microeconomic Simulation Engine
Vi Nhat Son
OmniEcon Nexus is an open-source, high-performance simulation engine for global microeconomic and macroeconomic analysis. Built with advanced deep learning, agent-based modeling, and optimization techniques, it enables detailed forecasting, risk analysis, policy generation, and portfolio optimization. This system supports up to 5 million agents and is designed as a comprehensive tool for governments, researchers, and developers to explore economic dynamics. Core Features Economic Forecasting: Predicts short-term and mid-term economic trends using deep learning models. Agent-Based Simulation: Models up to 5M agents (citizens, businesses, governments) with behavioral psychology. Portfolio Optimization: Optimizes asset allocation using the Sharpe ratio and real-time market data. Policy Generation: Automatically generates and evaluates macroeconomic policies with Q-learning. Risk Analysis: Assesses market volatility and systemic risk using network analysis. Market Psychology: Estimates PMI and agent psychological states (Fear, Greed, Complacency, Hope). Technical Overview Deep Learning Components MicroEconomicPredictor: Architecture: GRU, LSTM, Transformer Encoder, and a custom QuantumResonanceLayer. Configuration: Default hidden_dim=8192, num_layers=24, input_dim=72. Purpose: Forecasts short-term (short_pred) and mid-term (mid_pred) economic growth. Implementation: See MicroEconomicPredictor.forward() for details. QuantumResonanceLayer: Mechanism: Combines linear transformation with sinusoidal phase shifts and layer normalization. Purpose: Enhances prediction accuracy with quantum-inspired dynamics. Agent-Based Modeling HyperAgent: Roles: Citizens, businesses, governments. Attributes: Wealth, innovation, trade flow, resilience, psychological state. Behavior: Updated via interact(), influenced by market data, global context, and policies. Scale: Supports 5M agents with multiprocessing (Pool). Optimization and Policy Portfolio Optimization: Method: Uses scipy.optimize.minimize with SLSQP to maximize Sharpe ratio. Inputs: Short-term/mid-term predictions, volatility, crowd sentiment. Constraints: Total weights = 1, stocks + gold ≤ 80%. See: optimize_portfolio(). Policy Generation: Algorithm: Q-learning with state hashing (generate_policy()). Inputs: PMI, fear/greed indices, market momentum, volatility. Outputs: Policies like tax reduction, interest rate hikes, subsidies. Evaluation: Assesses impact via evaluate_policy_impact() using resilience, cash flow, consumption metrics. Network Analysis Systemic Risk Network: Structure: Directed graph (networkx.DiGraph) tracking trade dependencies. Metric: Systemic Risk Score (SRS) via calculate_systemic_risk_score() with betweenness centrality. Reflexive Network: Storage: Policy history in reflection_network. Retrieval: ANN-based (annoy) policy suggestions in suggest_reflexive_policy(). Real-Time Data Integration Sources: Yahoo Finance (yfinance): Market momentum, volatility, commodity prices. Twitter (tweepy): Crowd sentiment via hashtag analysis. World Bank (requests): Historical GDP, trade, inflation. Fallback: Simulated data if API keys are unavailable.
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