Evolution Of Complex Systems

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

Since the first edition sold out in less than a year, we now present the revised second edition of Mainzer's popular book. The theory of nonlinear complex systems has become a successful problem-solving approach in the natural sciences from laser physics, quantum chaos, and meteorology to computer simulations of cell growth in biology. It is now recognized that many of our social, ecological, and political problems are also of a global, complex, and nonlinear nature. And one of the most exciting contemporary topics is the idea that even the human mind is governed largely by the nonlinear dynamics of complex systems. In this wide-ranging but concise treatment, Prof. Mainzer discusses, in a nontechnical language, the common framework behind these endeavors. Emphasis is given to the evolution of new structures in natural and cultural systems and we see clearly how the new integrative approach can give insights not available from traditional reductionistic methods.

This book explores the universe and its subsystems from the three lenses of evolutionary (contingent), developmental (predictable), and complex (adaptive) processes at all scales. It draws from prolific experts within the academic disciplines of complexity science, physical science, information and computer science, theoretical and evo-devo biology, cosmology, astrobiology, evolutionary theory, developmental theory, and philosophy. The chapters come from a Satellite Meeting, "Evolution, Development and Complexity" (EDC) hosted at the Conference on Complex Systems, in Cancun, 2017. The contributions have been peer-reviewed and contributors from outside the conference were invited to submit chapters to ensure full coverage of the topics. This book explores many issues within the field of EDC such as the interaction of evolutionary stochasticity and developmental determinism in biological systems and what they might teach us about these twin processes in other complex systems. This text will appeal to students and researchers within the complex systems and EDC fields.

The book seeks unities among things often thought to be different from one another; but it also insists on the differences among things often squashed into unities.

Modularity—the attempt to understand systems as integrations of partially independent and interacting units—is today a dominant theme in the life sciences, cognitive science, and computer science. The concept goes back at least implicitly to the Scientific (or Copernican) Revolution, and can be found behind later theories of phrenology, physiology, and genetics; moreover, art, engineering, and mathematics rely on modular design principles. This collection broadens the scientific discussion of modularity by bringing together experts from a variety of disciplines, including artificial life, cognitive science, economics, evolutionary computation, developmental and evolutionary biology, linguistics, mathematics, morphology, paleontology, physics, theoretical chemistry, philosophy, and the arts. The contributors debate and compare the uses of modularity, discussing the different disciplinary contexts of "modular thinking" in general (including hierarchical organization, near-decomposability, quasi-independence, and recursion) or of more specialized concepts (including character complex, gene family, encapsulation, and mosaic evolution); what modules are, why and how they develop and evolve, and the implication for the research agenda in the disciplines involved; and how to bring about useful cross-disciplinary knowledge transfer on the topic. The book includes a foreword by the late Herbert A. Simon addressing the role of near-decomposability in understanding complex systems. Contributors: Lee Altenberg, Lauren W. Ancel-Meyers, Carl Anderson, Robert B. Brandon, Angela D. Buscalioni, Raffaele Calabretta, Werner Callebaut, Anne De Joan, Rafael Delgado-Buscalioni, Gunther J. Eble, Walter Fontana, Fernand Gobet, Alicia de la Iglesia, Slavik V. Jablan, Luigi Marengo, Daniel W. McShea, Jason Mezey, D. Kimbrough Oller, Domenico Parisi, Corrado Pasquali, Diego Rasskin-Gutman, Gerhard Schlosser, Herbert A. Simon, Roger D. K. Thomas, Marco Valente, Boris M. Velichkovsky, Gunter P. Wagner, Rasmus G. Winter Vienna Series in Theoretical Biology

"Malik demonstrates that management and management theory have strong foundations in systems science, and most specifically in a certain type of cybernetics of truly complex systems, of organismic, self-organizing, and evolving systems. This book provides the basics on how to create robust, functional, and sustainably viable systems. One of the reasons why it has become a classic on management cybernetics, now in its 11th edition, is that the strategies and heuristic principles of complexity management are still relevant - now more than ever."--Back cover.

The boundaries between simple and complicated, and complicated and complex system designations are fuzzy and debatable, even using quantitative measures of complexity. However, if you are a biomedical engineer, a biologist, physiologist, economist, politician, stock market speculator, or politician, you have encountered complex systems. Furthermore, your success depends on your ability to successfully interact with and manage a variety of complex systems. In order not to be blindsided by unexpected results, we need a systematic, comprehensive way of analyzing, modeling, and simulating complex systems to predict non-anticipated outcomes. In its engaging first chapters, the book introduces complex systems, Campbell's Law, and the Law of Unintended Consequences, and mathematics necessary for conversations in complex systems. Subsequent chapters illustrate concepts via commonly studied biological mechanisms. The final chapters focus on higher-level complexity problems, and introduce complexity in economic systems. Designed as a reference for biologists and biological engineers, Introduction to Complexity and Complex Systems lends itself to use in a classroom course to introduce advanced students studying biomedical engineering, biophysics, or physiology to complex systems. Engaging and illustrative, this book aids scientists and decision makers in managing biological complexity and complex systems.

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.