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Understanding Complex Systems George E. Mobus Michael C. Kalton Principles of Systems Science Springer Complexity Springer Complexity is an interdisciplinary program publishing the best research and aca- demic-level teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science. Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior, the manifestations of which are the spontaneous formation of distinctive temporal, spatial, or functional structures. Models of such systems can be successfully mapped onto quite diverse “real-life” situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the Internet, earthquake statistics and prediction, freeway traffi c, the human brain, or the formation of opinions in social systems, to name just some of the popular applications. Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms, and computational intelligence. The three major book publication platforms of the Springer Complexity program are the monograph series “Understanding Complex Systems” focusing on the various applications of complexity, the “Springer Series in Synergetics,” which is devoted to the quantitative theoretical and methodological foundations, and the “Springer Briefs in Complexity” which are concise and topical working reports, case studies, surveys, essays, and lecture notes of relevance to the fi eld. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works. Editorial and Programme Advisory Board Henry Abarbanel, Institute for Nonlinear Science, University of California, San Diego, USA Dan Braha, New England Complex Systems Institute and University of Massachusetts Dartmouth, USA Péter Érdi, Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy of Sciences, Budapest, Hungary Karl Friston, Institute of Cognitive Neuroscience, University College London, London, UK Hermann Haken, Center of Synergetics, University of Stuttgart, Stuttgart, Germany Viktor Jirsa, Centre National de la Recherche Scientifi que (CNRS), Universit’e de la M’editerran’ee, Marseille, France Janusz Kacprzyk, System Research, Polish Academy of Sciences,Warsaw, Poland Kunihiko Kaneko, Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan Scott Kelso, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA Markus Kirkilionis, Mathematics Institute and Centre for Complex Systems, University of Warwick, Coventry, UK Jürgen Kurths, Nonlinear Dynamics Group, University of Potsdam, Potsdam, Germany Andrzej Nowak, Department of Psychology, Warsaw University, Poland Linda Reichl, Center for Complex Quantum Systems, University of Texas, Austin, USA Peter Schuster, Theoretical Chemistry and Structural Biology, University of Vienna, Vienna, Austria Frank Schweitzer, System Design, ETH Zurich, Zurich, Switzerland Didier Sornette, Entrepreneurial Risk, ETH Zurich, Zurich, Switzerland Stefan Thurner, Section for Science of Complex Systems, Medical University of Vienna, Vienna, Austria Understanding Complex Systems Founding Editor: S. Kelso Future scientifi c and technological developments in many fi elds will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition – typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels – and in the rich diversity of behavior of which they are capable. The Springer Series in Understanding Complex Systems (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fi elds and endeavors. UCS is explicitly transdisciplinary. It has three main goals: fi rst, to elaborate the concepts, methods, and tools of complex systems at all levels of description and in all scientifi c fi elds, especially newly emerging areas within the life, social, behavioral, economic, and neuro- and cognitive sciences (and derivatives thereof); second, to encourage novel applications of these ideas in various fi elds of engineering and computation such as robotics, nano-technology, and informatics; and third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs, lecture notes, and selected edited contributions aimed at communicating new fi ndings to a large multidisciplinary audience. More information about this series at h ttp://www.springer.com/series/5394 George E. Mobus (cid:129) Michael C. Kalton Principles of Systems Science George E. Mobus Michael C. Kalton Associate Professor Professor Emeritus Faculty in Computer Science & Systems, Faculty in Interdisciplinary Arts & Sciences Computer Engineering & Systems University of Washington Tacoma Institute of Technology Tacoma , WA , USA University of Washington Tacoma Tacoma , WA , USA ISSN 1860-0832 ISSN 1860-0840 (electronic) ISBN 978-1-4939-1919-2 ISBN 978-1-4939-1920-8 (eBook) DOI 10.1007/978-1-4939-1920-8 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2014951029 © Springer Science+Business Media New York 2015 T his work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifi cally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. T he use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) About the Authors George E. Mobus i s an Associate Professor of Computer Science and Systems and Computer Engineering and Systems in the Institute of Technology at the University of Washington Tacoma. In addition to teaching computer science and engineering courses, he teaches courses in systems science to a broad array of students from across the campus. He received his Ph.D. in computer science from the University of North Texas in 1994. His dissertation, and sub- sequent research program at Western Washington University, involved developing autonomous robot agents by emulating natural intelligence as opposed to using some form of artifi cial intelligence. He is reviving this research agenda now that hardware elements have caught up with the processing require- ments for simulating real biological neurons. He also received an MBA from San Diego State University in 1983, doing a thesis on the modeling of decision support systems based on the hierarchical cybernetic principles presented in this volume. He did this while actually managing an embedded systems manufacturing and engineering company in Southern California. His baccalaureate degree was earned at the University of Washington (Seattle) in 1973, in zoology. He studied the energet- ics of living systems and the interplay between information, evolution, and complexity. By using some control algorithms that he had developed in both his undergraduate and MBA degrees in programming embedded control systems, he solved some interesting problems that led to promotion from a software engineer (without a degree) to the top spot in the company. All because of systems science! v vi About the Authors Michael C. Kalton is Professor Emeritus of Interdisciplinary Arts and Sciences at the University of Washington Tacoma. He came to systems science through the study of how cultures arise from and rein- force different ways of thinking about and interacting with the world. After receiving a Bachelor’s degree in Philosophy and Letters, a Master’s degree in Greek, and a Licentiate in Philosophy from St. Louis University, he went to Harvard University where in 1977 he received a joint Ph.D. degree in East Asian Languages and Civilizations, and Comparative Religion. He has done extensive research and publication on the Neo-Confucian tradition, the dominant intellectual and spiritual tradition throughout East Asia prior to the twentieth cen- tury. Environmental themes of self-organizing relational interdependence and the need to fi t in the patterned systemic fl ow of life drew his attention due to their reso- nance with East Asian assumptions about the world. Ecosystems joined social systems in his research and teaching, sharing a common matrix in the study of complex systems, emergence, and evolution. The interdisciplinary character of his program allowed this integral expansion of his work; systems thinking became the thread of continuity in courses ranging from the world’s great social, religious, and intellectual traditions to environmental ethics and the systems dynamics of contemporary society. He sees a deep and creative synergy between pre-modern Neo-Confucian thought and contemporary systems science; investigating this potential cross-fertil- ization is now his major research focus. Pref ace “Those that know do. Those that understand teach.” “The whole is more than the sum of its parts.” Aristotle “True wisdom comes to each of us when we realize how little we understand about life, ourselves, and the world around us.” Socrates “Our species needs, and deserves, a citizenry with minds wide awake and a basic understanding of how the world works.” Carl Sagan Understanding This book is about u nderstanding . When can a person say that they understand something? Is understanding different from what we normally call “knowledge?” Do we actually understand a phenomenon when we can make predictions about its behavior? Perhaps an example of the latter question will serve as a key to the con- cept of understanding. C onsider the law of g ravity. We all know what gravity is; who hasn’t experienced its insistence on one being pulled toward the Earth, sometimes painfully? Yet it is the case that we actually still do not really understand gravity in the sense of what causes this force to act upon mass. Sir Isaac Newton formulated the laws of motion and particularly the mechanics of planetary motions from Johannes Kepler’s planetary “laws.” Kepler, in turn, had derived his laws from discovering the patterns contained in Tycho Brahe’s astronomical observations of the planets’ motions. Newton invented a descriptive language, the calculus, and advanced the universal vii viii Preface laws of gravitation as a formula 1 that would predict with reasonable accuracy (even today) how bodies behave when acted upon by its force (one of four fundamental forces of nature2 ). NASA engineers can predict with tremendous accuracy just how much time and with what force a small rocket engine should fi re to maintain a tra- jectory of a space probe millions of miles from Earth so that it neatly passes by a moon of Saturn to get pictures and data. A lbert Einstein “improved” our ability to predict such behavior, indeed for all objects of all masses and all distances in the universe, with his theory of General Relativity. Rather than describe this behavior as resulting from a mysterious force, Einstein converted the language of gravitation to geometry, explaining how the behavior of objects, such as planets orbiting the Sun, is a consequence of the distor- tions in space (and for really fast objects, time). Both theories provide adequate predictions for celestial mechanics. We can say we humans understand the behavior from the outside. That is, we can, given the initial conditions of any two bodies of known masses at time 0, predict with great accuracy and very impressive precision what will happen in the future. But, and this is a crucial “but,” we don’t know why g ravity works the way it does. For example, just saying that space is curved in the region of a massive object doesn’t begin to say why. Physics is still actively seeking that kind of understanding. Our knowledge includes the formulas needed to predict planetary and satellite motions, which we routinely use, but it does not include the internal workings of nature suffi cient to explain why those formulas work. And this condition, what we must call “partial understanding,” is often more true of much of our knowledge than we might like to acknowledge. Systems science is ultimately about gaining more complete understanding. Notice we said “more complete” rather than merely “complete.” Understanding comes in degrees. As far as anyone knows, there is no such thing as absolute (complete) understanding or knowledge (see our discussion of knowledge in Chap. 7 ) . Rather there are approaches to understanding more about phenomena by gaining knowledge of their inner mechanics. All of the sciences work at this. In this regard systems science can be considered the universal science. All sciences seek to gain and organize knowledge systematically. They all use methodologies that, while geared to the specifi c domain of interest (say physics or psychology), nevertheless are variations on concepts you will fi nd in this volume. They all seek to establish organizations of knowledge (invariably hierarchical in nature) that expose patterns of relations, for example, Dmitri Mendeleev’s Periodic Table for chemistry (and its many improvements since then) or Carolus Linnaeus’ classifi ca- tion hierarchy for species that helped lead to the Theory of Evolution proposed by Charles Darwin . As you will see in this text, organization, structure, and many other aspects of knowledge form the kernel of systems science . 1 F = G ( m m/ r 2 ). F is the force due to gravitational attraction. m and m are the masses of the two 1 2 1 2 bodies (it takes two!) and r is the distance between the centers of the two bodies. 2 T he other three being electromagnetic, weak, and strong f orces. The fi rst of these describes how elementary particles behave due to attraction and repulsion. The latter two apply to interactions between components of atomic nuclei. Preface ix What systems science does, above and beyond the efforts of any of the domain- oriented sciences, is to make the whole enterprise of gaining better understanding explicit. All scientists (in the broadest interpretation of that word) are systems scientists to one degree or another, even when they don’t know that. Mental Models of the World: Cognitive Understanding Whenever you think about what may happen during an upcoming day in your life you are accessing what we call a m ental model of your world. As will be described in several sections of this book, our brains construct these models based on our experiences as we grow up and age. Most of our knowledge is tucked away in what cognitive scientists call implicit form. This could be “procedural” knowledge, such as how to ride a bicycle or drive a car, or it could be more general knowledge that isn’t automatically accessible to conscious thinking; you need to expend some mental effort to do so. Your ability to live in a society with a culture and to go about daily life all depends on your having built up a large repertoire of mental models about how things work. When you enter a restaurant, for example, you know basically what to do without even thinking about it. You know how to wait to be seated, how to examine a menu and decide your order, how to give your order to a server, etc. You have done this so often that it is like second nature. The places and people and menus may change, but you know the general script for how to behave and accomplish your goal (getting fed!). Perhaps as much as 80–90 % of your daily interactions with things and people are the result of processing these mental models subconsciously! Models are manipulatable representations of things (especially people), relations of things, and how they behave in the world. Mental models are those we build up in our neural network systems in, especially, our neocortex. Our understanding of the world depends on us being able to learn what to expect from the things and people we interact with into the possible future. W e will have much more to say about mental models in Chaps. 7 – 9 (Part III). What we intend for this book to accomplish is to help you organize your m ental models , to make connections between aspects of the world you may not have explicitly recognized. We believe that systems science is capable of helping people make more sense of their mental models—to help them better understand the world. Formal Models of the World: The Extension of Cognitive Understanding One of the great achievements of the human mind has been to develop abstract, external representations of the world. This started with the evolution of language (maybe 150–200 thousand years ago) as a way to communicate complex mental