In this volume, some of the world's leading scientists discuss the role of complexity across all the scientific disciplines. Opinions differ: for some, complexity holds the key to a deeper and fuller understanding of the world; to others, it is merely a modern version of the philsophers' stone.
This book aims to develop models and modeling techniques that are useful when applied to all complex systems. It adopts both analytic tools and computer simulation. The book is intended for students and researchers with a variety of backgrounds.
The Emergence of Everything is a study of complexity which highlights 28 moments of , what the the author feels are, the most important emergences. The author also seeks out the nature of God in an emergent universe, agruing that we can know God through a study of the laws of nature.
The aim of this book is to show how supramolecular complexity of cell organization can dramatically alter the functions of individual macromolecules within a cell. The emergence of new functions which appear as a consequence of supramolecular complexity, is explained in terms of physical chemistry. The book is interdisciplinary, at the border between cell biochemistry, physics and physical chemistry. This interdisciplinarity does not result in the use of physical techniques but from the use of physical concepts to study biological problems. In the domain of complexity studies, most works are purely theoretical or based on computer simulation. The present book is partly theoretical, partly experimental and theory is always based on experimental results. Moreover, the book encompasses in a unified manner the dynamic aspects of many different biological fields ranging from dynamics to pattern emergence in a young embryo. The volume puts emphasis on dynamic physical studies of biological events. It also develops, in a unified perspective, this new interdisciplinary approach of various important problems of cell biology and chemistry, ranging from enzyme dynamics to pattern formation during embryo development, thus paving the way to what may become a central issue of future biology.
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.
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.
The emergence of a true systemic science - the systemic one - capable of rigorously addressing the many problems posed by the design and management of the evolution of modern complex systems is therefore urgently needed if wants to be able to provide satisfactory answers to the many profoundly systemic challenges that humanity will have to face at the dawn of the third millennium. This emergence is of course not easy because one can easily understand that the development of the systemic is mechanically confronted with all the classical disciplines which can all pretend to bring part of the explanations necessary to the understanding of a system and which do not naturally see a good eye a new discipline claim to encompass them in a holistic approach ... The book of Jacques Printz is therefore an extremely important contribution to this new emerging scientific and technical discipline: it is indeed first of all one of the very few "serious" works published in French and offering a good introduction to the systemic. It gives an extremely broad vision of this field, taking a thread given by the architecture of systems, in other words by the part of the systemic that is interested in the structure of systems and their design processes, which allows everyone to fully understand the issues and issues of the systemic. We can only encourage the reader to draw all the quintessence of the masterful work of Jacques Printz which mixes historical reminders explaining how the systemic emerged, introduction to key concepts of the systemic and practical examples to understand the nature and the scope of the ideas introduced.
The book gathers articles that were exposed during the seventh edition of the Workshop ?Data Analysis in Astronomy?. It illustrates a current trend to search for common expressions or models transcending usual disciplines, possibly associated with some lack in the Mathematics required to model complex systems. In that, data analysis would be at the epicentre and a key facilitator of some current integrative phase of Science.It is all devoted to the question of ?representation in Science?, whence its name, IMAGe IN AcTION, and main thrusts Part A: Information: data organization and communication, Part B: System: structure and behaviour, Part C: Data ? System representation. Such a classification makes concepts as ?complexity? or ?dynamics? appear like transverse notions: a measure among others or a dimensional feature among others.Part A broadly discusses a dialogue between experiments and information, be information extracted-from or brought-to experiments. The concept is fundamental in statistics and tailors to the emergence of collective behaviours. Communication then asks for uncertainty considerations ? noise, indeterminacy or approximation ? and its wider impact on the couple perception-action. Clustering being all about uncertainty handling, data set representation appears not to be the only solution: Introducing hierarchies with adapted metrics, a priori pre-improving the data resolution are other methods in need of evaluation. The technology together with increasing semantics enables to involve synthetic data as simulation results for the multiplication of sources.Part B plays with another couple important for complex systems: state vs. transition. State-first descriptions would characterize physics, while transition-first would fit biology. That could stem from life producing dynamical systems in essence. Uncertainty joining causality here, geometry can bring answers: stable patterns in the state space involve constraints from some dynamics consistency. Stable patterns of activity characterize biological systems too. In the living world, the complexity ? i.e. a global measure on both states and transitions ? increases with consciousness: this might be a principle of evolution. Beside geometry or measures, operators and topology have supporters for reporting on dynamical systems. Eventually targeting universality, the category theory of topological thermodynamics is proposed as a foundation of dynamical system understanding.Part C details examples of actual data-system relations in regards to explicit applications and experiments. It shows how pure computer display and animation techniques link models and representations to ?reality? in some ?concrete? virtual, manner. Such techniques are inspired from artificial life, with no connection to physical, biological or physiological phenomena! The Virtual Observatory is the second illustration of the evidence that simulation helps Science not only in giving access to more flexible parameter variability, but also due to the associated data and method storing-capabilities. It fosters interoperability, statistics on bulky corpuses, efficient data mining possibly through the web etc. in short a reuse of resources in general, including novel ideas and competencies. Other examples deal more classically with inverse modelling and reconstruction, involving Bayesian techniques or chaos but also fractal and symmetry.
