Researchers from Greece, Tunisia, and Italy have introduced an innovative artificial intelligence technique designed to manage the Uzawa-Lucas endogenous growth model, a key framework in economic theory that explores how physical and human capital jointly influence long-term development. Unlike conventional control methods that rely on complete system models, this new AI-powered approach operates effectively even when critical economic parameters are uncertain or partially observed. n nThe method leverages a mathematical concept known as ‘differential flatness,’ which enables transformation of the inherently nonlinear economic system into a simplified linear structure. By identifying a specific ‘flat output’—a variable tied to physical capital accumulation—the entire system’s behavior can be reconstructed and guided toward desired outcomes. This allows policymakers to influence growth trajectories without requiring full knowledge of underlying dynamics. n nAt the core of the system is an adaptive fuzzy controller enhanced with neuro-fuzzy approximators, a form of machine learning that adjusts in real time to unknown or changing conditions. To compensate for incomplete data, the controller integrates a state observer that estimates unmeasured variables, functioning like a virtual dashboard for economic indicators. This output feedback mechanism ensures practical usability in real-world settings where comprehensive data is often unavailable. n nCrucially, the design incorporates Lyapunov stability theory to mathematically guarantee that the economy will converge to targeted growth paths, regardless of initial disturbances or parameter uncertainties. This global stability assurance enhances its reliability for policy simulation and implementation. n nLead researcher Dr. Gerasimos Rigatos emphasized the method’s robustness, noting that it transforms theoretical economic models into actionable tools for stabilization and planning. The approach could support decision-making in areas such as sustainable development and economic transitions, where uncertainty is high and data limited. n nAlthough currently validated through simulation across various scenarios, the team anticipates extending the framework to other macroeconomic systems. This work marks a significant step toward model-free, AI-driven strategies for economic governance. n— news from EurekAlert!n
— News Original —nNew AI controller stabilizes complex economic growth modelsn[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. n nWhat ‘s New? n nThe 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. n nThis innovative method: n nProves and leverages the model ‘s “differential flatness”, a mathematical property that allows the complex economic model to be transformed into a simplified, linear form. n nActs as a “model-free” controller, learning the economy ‘s unknown dynamics in real-time using neuro-fuzzy approximators, a type of AI. n nWorks with incomplete information, using a state observer to estimate unmeasured economic variables, much like a virtual economic dashboard. n nGuarantees global stability through rigorous mathematical analysis (Lyapunov theory), ensuring the economy converges to the target trajectory. n nHow It Works n nThe 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. n nWhy It Matters n nEconomic 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. n n”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.” n nThe 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. n nWhat ‘s Next? n nWhile 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.