Decoding Economic Complexity
PNG Group is at the forefront of AI research, developing next-generation models for the economic calculation and analysis of highly complex systems.
Discover Our MissionOur Mission
Traditional economic models struggle to capture the intricate, dynamic, and often chaotic nature of modern global systems. The sheer volume of variables, non-linear interactions, and feedback loops makes accurate prediction and planning nearly impossible.
Our mission is to bridge this gap. We leverage advanced artificial intelligence, machine learning, and quantum-inspired algorithms to build predictive frameworks that can simulate and analyze economies with unprecedented accuracy and depth. We aim to provide tools that empower better decision-making for a more stable and prosperous future.
Core Technologies
Our research is built upon a foundation of cutting-edge computational techniques.
Neural Network Arbitrage
We employ deep learning architectures to identify and model non-obvious relationships within vast datasets, uncovering arbitrage opportunities and systemic risks invisible to traditional analysis.
Agent-Based Modeling
Our AI creates millions of autonomous agents, each with unique behaviors and goals, to simulate economies from the ground up. This provides a granular view of emergent market phenomena.
Probabilistic Computing
Instead of deterministic predictions, our models generate probability distributions for future economic states, offering a more nuanced and realistic understanding of uncertainty and potential outcomes.
Our Research Focus
Supply Chain Resilience
Modeling global supply chains to predict bottlenecks and vulnerabilities in response to geopolitical, environmental, and economic shocks.
Macro-Financial Stability
Analyzing the interplay between financial markets and the real economy to identify sources of systemic risk and prevent cascading failures.
Automated Policy Design
Using reinforcement learning to discover optimal economic policies that balance growth, equity, and sustainability in complex simulations.