Complexity is emerging as a post-Newtonian paradigm for approaching a large body of phenomena of concern at the crossroads of physical, engineering, environmental, life and human sciences from a unifying point of view. This book outlines the foundations of modern complexity research as it arose from the cross-fertilization of ideas and tools from nonlinear science, statistical physics and numerical simulation. It is shown how these developments lead to an understanding, both qualitative and quantitative, of the complex systems encountered in nature and in everyday experience and, conversely, how natural complexity acts as a source of inspiration for progress at the fundamental level.

Complexity is emerging as a post-Newtonian paradigm for approaching a large body of phenomena of concern at the crossroads of physical, engineering, environmental, life and human sciences from a unifying point of view. This book outlines the foundations of modern complexity research as it arose from the cross-fertilization of ideas and tools from nonlinear science, statistical physics and numerical simulation. It is shown how these developments lead to an understanding, both qualitative and quantitative, of the complex systems encountered in nature and in everyday experience and, conversely, how natural complexity acts as a source of inspiration for progress at the fundamental level.

Analyzes approaches to the study of complexity in the physical, biological, and social sciences.

The Designing Environments book series addresses questions regarding necessary environmental transformation in the context of the fast-unfolding environmental crisis. This is done from a broad interdisciplinary perspective, examining the negative impact of human transformations of the environment and providing different inroads towards sustainable environmental transformation with net positive impact. Volume one of the Designing Environments book series brings together experts from different disciplines and often inter- and transdisciplinary contexts, who discuss specific approaches to overcoming the negative impact of the transformation of environments by humans. Across the 12 chapters of volume one, specific keywords recur that are indicative of shared insights and concerns. These include Anthropocene, climate change, complexity, critical zone, ecosystem services, and sustainability. Furthermore, interdisciplinary approaches to human–environment interactions, sustainability transitions, and socio-ecological systems take center stage and are discussed in relation to conceptual and methodological as well as societal and technological challenges and opportunities.

Chaos: from simple models to complex systems aims to guide science and engineering students through chaos and nonlinear dynamics from classical examples to the most recent fields of research. The first part, intended for undergraduate and graduate students, is a gentle and self-contained introduction to the concepts and main tools for the characterization of deterministic chaotic systems, with emphasis to statistical approaches. The second part can be used as a reference by researchers as it focuses on more advanced topics including the characterization of chaos with tools of information theory and applications encompassing fluid and celestial mechanics, chemistry and biology. The book is novel in devoting attention to a few topics often overlooked in introductory textbooks and which are usually found only in advanced surveys such as: information and algorithmic complexity theory applied to chaos and generalization of Lyapunov exponents to account for spatiotemporal and non-infinitesimal perturbations. The selection of topics, numerous illustrations, exercises and proposals for computer experiments make the book ideal for both introductory and advanced courses. Sample Chapter(s). Introduction (164 KB). Chapter 1: First Encounter with Chaos (1,323 KB). Contents: First Encounter with Chaos; The Language of Dynamical Systems; Examples of Chaotic Behaviors; Probabilistic Approach to Chaos; Characterization of Chaotic Dynamical Systems; From Order to Chaos in Dissipative Systems; Chaos in Hamiltonian Systems; Chaos and Information Theory; Coarse-Grained Information and Large Scale Predictability; Chaos in Numerical and Laboratory Experiments; Chaos in Low Dimensional Systems; Spatiotemporal Chaos; Turbulence as a Dynamical System Problem; Chaos and Statistical Mechanics: Fermi-Pasta-Ulam a Case Study. Readership: Students and researchers in science (physics, chemistry, mathematics, biology) and engineering.

This book constitutes the revised selected papers of the 14th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2019, held in Rende, Italy, in September 2019. The 13 full papers and 4 short paper presented were thoroughly reviewed and selected from 31 submissions. They are focused on the topics of information systems, design and analysis of algorithms, artificial intelligence, machine learning, cognitive science, modeling and simulation, collaborative and social computing, parallel computing, distributed computing. Chapters 14, 15, 16, and 17 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book on complexity science comprises a collection of chapters on methods and principles from a wide variety of disciplinary fields — from physics and chemistry to biology and the social sciences.In this two-part volume, the first part is a collection of chapters introducing different aspects in a coherent fashion, and providing a common basis and the founding principles of the different complexity science approaches; the next provides deeper discussions of the different methods of use in complexity science, with interesting illustrative applications.The fundamental topics deal with self-organization, pattern formation, forecasting uncertainties, synchronization and revolutionary change, self-adapting and self-correcting systems, and complex networks. Examples are taken from biology, chemistry, engineering, epidemiology, robotics, economics, sociology, and neurology.