{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/omniecon-nexus-global-microeconomic","title":"OmniEcon Nexus: Global Microeconomic Simulation Engine","arxiv_id":null,"date":"2025-04-07","proceeding":"Independent publication 2025 4","authors":["Vi Nhat Son"],"abstract":"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.\r\n\r\nCore Features\r\nEconomic Forecasting: Predicts short-term and mid-term economic trends using deep learning models.\r\nAgent-Based Simulation: Models up to 5M agents (citizens, businesses, governments) with behavioral psychology.\r\nPortfolio Optimization: Optimizes asset allocation using the Sharpe ratio and real-time market data.\r\nPolicy Generation: Automatically generates and evaluates macroeconomic policies with Q-learning.\r\nRisk Analysis: Assesses market volatility and systemic risk using network analysis.\r\nMarket Psychology: Estimates PMI and agent psychological states (Fear, Greed, Complacency, Hope).\r\nTechnical Overview\r\nDeep Learning Components\r\nMicroEconomicPredictor:\r\n\r\nArchitecture: GRU, LSTM, Transformer Encoder, and a custom QuantumResonanceLayer.\r\nConfiguration: Default hidden_dim=8192, num_layers=24, input_dim=72.\r\nPurpose: Forecasts short-term (short_pred) and mid-term (mid_pred) economic growth.\r\nImplementation: See MicroEconomicPredictor.forward() for details.\r\nQuantumResonanceLayer:\r\n\r\nMechanism: Combines linear transformation with sinusoidal phase shifts and layer normalization.\r\nPurpose: Enhances prediction accuracy with quantum-inspired dynamics.\r\nAgent-Based Modeling\r\nHyperAgent:\r\nRoles: Citizens, businesses, governments.\r\nAttributes: Wealth, innovation, trade flow, resilience, psychological state.\r\nBehavior: Updated via interact(), influenced by market data, global context, and policies.\r\nScale: Supports 5M agents with multiprocessing (Pool).\r\nOptimization and Policy\r\nPortfolio Optimization:\r\n\r\nMethod: Uses scipy.optimize.minimize with SLSQP to maximize Sharpe ratio.\r\nInputs: Short-term/mid-term predictions, volatility, crowd sentiment.\r\nConstraints: Total weights = 1, stocks + gold ≤ 80%.\r\nSee: optimize_portfolio().\r\nPolicy Generation:\r\n\r\nAlgorithm: Q-learning with state hashing (generate_policy()).\r\nInputs: PMI, fear/greed indices, market momentum, volatility.\r\nOutputs: Policies like tax reduction, interest rate hikes, subsidies.\r\nEvaluation: Assesses impact via evaluate_policy_impact() using resilience, cash flow, consumption metrics.\r\nNetwork Analysis\r\nSystemic Risk Network:\r\nStructure: Directed graph (networkx.DiGraph) tracking trade dependencies.\r\nMetric: Systemic Risk Score (SRS) via calculate_systemic_risk_score() with betweenness centrality.\r\nReflexive Network:\r\nStorage: Policy history in reflection_network.\r\nRetrieval: ANN-based (annoy) policy suggestions in suggest_reflexive_policy().\r\nReal-Time Data Integration\r\nSources:\r\nYahoo Finance (yfinance): Market momentum, volatility, commodity prices.\r\nTwitter (tweepy): Crowd sentiment via hashtag analysis.\r\nWorld Bank (requests): Historical GDP, trade, inflation.\r\nFallback: Simulated data if API keys are unavailable.","url_abs":"https://zenodo.org/records/15169285","url_pdf":"https://zenodo.org/records/15169285/files/Whitepaper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"omniecon-nexus-global-microeconomic","repo_url":"https://github.com/vinhatson/OmniEcon-Nexus-Global-Microeconomic-Simulation-Engine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"portfolio-optimization","task_name":"Portfolio Optimization"},{"task_slug":"q-learning","task_name":"Q-Learning"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gru","method_name":"GRU"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"q-learning","method_name":"Q-Learning"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}