Applied Antifragility in Natural Systems

From Principles to Applications
Erscheinungsjahr 2025
ISBN: 978-3-031-90390-8
Verlag: Springer Nature Switzerland

Buch, Englisch, 80 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 160 g

Reihe: SpringerBriefs in Computer Science

As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. Such principles are already well suited for describing behaviors in natural systems but also in approaching therapy designs and eco-system modelling and eco-system analysis.

The purpose of this book is to build a foundational knowledge base by applying antifragile system design, analysis, and development in natural systems, including biomedicine, neuroscience, and ecology as main fields. We are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop systems that behave beyond robust in the face of uncertainty when characterizing and intervening in biomedical and ecological (eco)systems.

The book introduces the framework of applied antifragility and possible paths to build systems that gain from uncertainty. We draw from the body of literature on natural systems (e.g. cancer therapy, antibiotics, neuroscience, and agricultural pest management) in an attempt to unify the scales of antifragility in one framework. The work of the Applied Antifragility Group in oncology, neuroscience, and ecology led by the authors provides a good overview on the current research status.

Zielgruppe

Research

Autoren/Hrsg.

Axenie, Cristian

West, Jeffrey

Bauer, Roman

López Corona, Oliver

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation

Inhaltsverzeichnis

Introduction.- Ecological Antifragility.- Evolutionary Antifragility.- Interventional Antifragility.- Conclusions.

Über Autor(innen)

Cristian Axenie is High Tech Agenda Bayern Professor of Artificial Intelligence, Scientific Staff with the Center of Artificial Intelligence, and Research Group Leader of the Sensorimotor Processing Intelligence and Control in Efficient compute Systems Laboratory (SPICES Lab) at the Technische Hochschule Nürnberg Georg Simon Ohm (Nuremberg Institute of Technology) in Nürnberg, Germany. After earning a Dr. Eng. Sc. in Neuroscience and Robotics from the Technical University of Munich (TUM), Dr Axenie was a Research Fellow in Neuromorphic Engineering with the TUM Center of Competence Neuroengineering before joining Huawei Research Center in Munich. With Huawei, Dr Axenie was a Staff Research Engineer in Enterprise Intelligence for Cloud Solutions with Huawei's largest research center outside China. At the same time, Dr Axenie was Head of the Laboratory and Principal Scientist at the Audi Konfuzius-Institut Ingolstadt Laboratory at the Technical University of Ingolstadt, a Sino-German research initiative focused on Human-centered Artificial Intelligence. Earlier, Dr Axenie earned a B. Sc. in Control Engineering and a M. Sc. in Robotics and AI from the University of Galati in Romania. He has over 15 years of experience in academic research and over 10 years in industrial research. His research has been published in 50+ peer-reviewed publications and 10+ patents.

Roman Bauer is a Senior Lecturer (Associate Professor) at the Nature Inspired Computing and Engineering research group, in the Computer Science Research Centre at the University of Surrey (UK). He received his Bachelor's and Master's Degree in Computational Science and Engineering from ETH Zuerich, Switzerland. Afterwards, he did his doctoral studies at the Institute for Neuroinformatics (ETH Zürich/Uni Zürich) on simulations of brain development. He then joined Newcastle University
(UK) in 2013 as a postdoc and afterwards led his own lab funded by two fellowships (MRC Skills Development Fellowship and EPSRC UKRI Innovation Fellowship). In 2020 he became a Lecturer and in 2023 a Senior Lecturer at the University of Surrey, where he since then leads the interdisciplinary COMBYNE research lab. Dr Bauer's research focuses on the computational modelling and analysis of biological dynamics, in particular those of the brain. Core components of his interests are neurodevelopment and neurodegeneration. His highly interdisciplinary research involves modern computing approaches, biological expertise, innovative machine learning methods and IT- related collaboration. 

Oliver López-Corona is a theoretical physicist who studies the physics of life (both natural and artificial), from its theoretical foundations to specific applications in the health of ecosystems, including the human ecosystem.

Jeffrey West is an Assistant Professor in the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, where he is an active member of the Center of Excellence for Evolutionary Therapy. His educational background began with a B. Sc. in mechanical engineering from Ohio Northern University with a focus on dynamical systems. His Ph.D. studies at the University of Southern California applied evolutionary game theory and high performance computing methods to study the evolution and ecology of cancer progression and treatment. Now, the goal of his research program at Moffitt is to aid in targeting treatment resistance by constructing mathematical models of 1) tumor evolution and heterogeneity and 2) evolutionary-minded treatment strategies, employing techniques such as agent-based modeling, dose response convexity analysis and evolutionary game theory.