Modelling Resilience in Complex Systems. Emergent Risk, Uncertainty, Probabilistic Machine Learning, Resilience Engineering - Vairo Tomaso

Taming the Unknown: The Science of Emergent Risk

In an era defined by rapid technological change, interconnected infrastructures, and environmental challenges, understanding and managing emergent risks in complex systems has become a critical scientific and societal priority. Traditional risk management approaches, often static and siloed, are inadequate for addressing uncertainties that are dynamic, systemic, and poorly understood.

This book addresses a pressing gap in the literature by offering a comprehensive and integrative framework that combines the science of uncertainty, emergent risk, and advanced machine learning methods — particularly Bayesian and probabilistic models. While there is abundant research on individual aspects of uncertainty, risk, and AI, few resources synthesize these elements within a unified perspective oriented toward real-world applications, including energy transition, healthcare systems, and Industry 5.0.

By blending theoretical foundations with practical case studies, the book serves both as a scholarly reference and a guide for practitioners, policymakers, and researchers. It contributes to the advancement of dynamic risk assessment and resilience engineering, disciplines that are essential for designing adaptive, safer, and more sustainable systems in the face of unpredictability.

4 Main part: topic, subject matter of the book

Topic:

The book explores the intersection of uncertainty, emergent risk, and machine learning within complex socio-technical systems. It provides a scientific and methodological foundation for understanding, modeling, and managing risks that arise unpredictably from system interactions, using advanced data-driven techniques.

Subject Matter: The core subject matter of the book covers:

·       Basics on process safety and occupational safety evolution.

·       Uncertainty in Complex Systems: Definitions, typologies (aleatory vs. epistemic), and their implications for systemic risk and emergence.

·       Emergent Risk: The nature, characteristics, and modeling of risks that emerge from dynamic, non-linear interactions in complex environments.

·       Machine Learning and Probabilistic Methods: How techniques such as Bayesian inference, probabilistic graphical models, Hidden Markov Models, and Bayesian deep learning can be applied to quantify uncertainty and predict emergent risks.

·       Resilience Engineering: Strategies and frameworks for building systems capable of anticipating, adapting, and responding to unexpected risks, with the support of machine learning tools.

·       Applications: Real-world case studies in sectors such as energy transition, healthcare, digital industry, and critical infrastructure, demonstrating practical applications of the proposed methodologies.

·       Ethics and Future Perspectives: Ethical considerations in the use of AI and machine learning for risk management, transparency, and the role of hybrid human-machine intelligence.

This multidisciplinary approach positions the book at the convergence of risk science, machine learning, systems engineering, and decision-making under uncertainty.

4 Final sentence: conclusion, relevance of your book to the target group.

This book offers a timely and essential contribution to the fields of risk analysis, systems engineering, and data-driven decision-making. By integrating cutting-edge machine learning methods with a deep understanding of uncertainty and emergent risks, it provides readers with both the theoretical grounding and practical tools needed to navigate the complexities of modern systems.

The book is particularly relevant for:

·       Researchers and Academics in risk science, complex systems, artificial intelligence, and resilience engineering.

·       Industry Professionals and Engineers working in sectors such as energy, healthcare, manufacturing, and critical infrastructure.