New AI controller stabilizes complex economic growth models

[Greece/Tunisia/Italy] – A team of international researchers has developed a groundbreaking artificial intelligence (AI) method to control and stabilize the Uzawa-Lucas endogenous growth model, a cornerstone economic theory that describes the interaction between physical capital (like machinery) and human capital (like skills and knowledge). This novel approach, which functions without needing a precise mathematical model of the economy, could provide a powerful new tool for economists and policymakers to design more stable and effective economic policies.

What's New?
The Uzawa-Lucas model is fundamental to understanding long-term economic growth but is notoriously complex and nonlinear. Controlling such a model—guiding its variables like capital ratios towards desired targets—is a significant challenge, especially when key parameters are unknown or changing. Traditional methods struggle with this uncertainty. To address this, Dr. Gerasimos Rigatos and colleagues from Greece, Tunisia, and Italy have designed a flatness-based adaptive fuzzy controller that uses only partial economic data (output feedback) to stabilize the entire system.

This innovative method:

• Proves and leverages the model's "differential flatness", a mathematical property that allows the complex economic model to be transformed into a simplified, linear form.

• Acts as a "model-free" controller, learning the economy's unknown dynamics in real-time using neuro-fuzzy approximators, a type of AI.

• Works with incomplete information, using a state observer to estimate unmeasured economic variables, much like a virtual economic dashboard.

• Guarantees global stability through rigorous mathematical analysis (Lyapunov theory), ensuring the economy converges to the target trajectory.

How It Works
The controller treats the economic model like a dynamic system to be controlled. By identifying a special "flat output" (in this case, a variable related to the physical capital sector), the entire model can be simplified. An adaptive fuzzy AI system then continuously learns and compensates for the model's uncertainties. A key feature is that it only requires feedback from a single output, making it practical for real-world scenarios where full economic data is scarce. The control signal, which can be interpreted as a policy lever like the discount rate, is automatically adjusted to steer the economy toward stability.

Why It Matters
Economic models are essential for forecasting and policy, but their inherent complexity and uncertainty often limit their practical application for precise control. This research bridges that gap.
"This approach is robust and flexible, making it possible to stabilize complex economic growth models even with limited data and significant uncertainty," said Dr. Gerasimos Rigatos, the study's lead author. "It transforms the model from a descriptive tool into a actionable one for stability analysis and policy design."

The method's ability to ensure stability under unknown conditions opens new possibilities for simulating and implementing economic policies, from managing sustainable growth to navigating periods of economic transition.

What's Next?
While the method was validated through comprehensive simulations showing successful stabilization across multiple test cases, the researchers envision its application extending to other complex macroeconomic models. This work paves the way for a new class of AI-driven, model-free economic control strategies.

About the Research
This research, titled "A flatness-based adaptive fuzzy control method for an endogenous economic growth model", was published in Artificial Intelligence and Autonomous Systems.
Full article: https://doi.org/10.55092/aias20250008