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The Self-Organizing Economy In the last few years the concept of self-organizing systems—complex systems in which randomness and chaos seem spontaneously to evolve into unexpected order—has linked together researchers in many fields, from artificial intelligence to chemistry, from evolution to geology. Now leading economist Paul Krugman shows how principles that explain the growth of hurricanes and embryos can also explain the formation of cities and business cycles; how the same principles of “order from random growth” can explain the strangely simple rules that describe the sizes of earthquakes, meteorites, and metropolitan areas. Weaving together strands from many disciplines, from location theory to biology, The Self-Organizing Economy offers a surprising new view of how the economy structures itself in space and time.
The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns--phenomena that have fascinated naturalists for centuries--a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology--a field of study at the forefront of life sciences research.
Can physics be an appropriate framework for the understanding of ecological science? Most ecologists would probably agree that there is little relation between the complexity of natural ecosystems and the simplicity of any example derived from Newtonian physics. Though ecologists have long been interested in concepts originally developed by statistical physicists and later applied to explain everything from why stock markets crash to why rivers develop particular branching patterns, applying such concepts to ecosystems has remained a challenge. Self-Organization in Complex Ecosystems is the first book to clearly synthesize what we have learned about the usefulness of tools from statistical physics in ecology. Ricard Solé and Jordi Bascompte provide a comprehensive introduction to complex systems theory, and ask: do universal laws shape the structure of ecosystems, at least at some scales? They offer the most compelling array of theoretical evidence to date of the potential of nonlinear ecological interactions to generate nonrandom, self-organized patterns at all levels. Tackling classic ecological questions--from population dynamics to biodiversity to macroevolution--the book's novel presentation of theories and data shows the power of statistical physics and complexity in ecology. Self-Organization in Complex Ecosystems will be a staple resource for years to come for ecologists interested in complex systems theory as well as mathematicians and physicists interested in ecology.
Self-organized criticality, the spontaneous development of systems to a critical state, is the first general theory of complex systems with a firm mathematical basis. This theory describes how many seemingly desperate aspects of the world, from stock market crashes to mass extinctions, avalanches to solar flares, all share a set of simple, easily described properties. "...a'must read'...Bak writes with such ease and lucidity, and his ideas are so intriguing...essential reading for those interested in complex systems...it will reward a sufficiently skeptical reader." -NATURE "...presents the theory (self-organized criticality) in a form easily absorbed by the non-mathematically inclined reader." -BOSTON BOOK REVIEW "I picture Bak as a kind of scientific musketeer; flamboyant, touchy, full of swagger and ready to join every fray... His book is written with panache. The style is brisk, the content stimulating. I recommend it as a bracing experience." -NEW SCIENTIST
During the past twenty years, a broad spectrum of theories and methods have been developed in physics, chemistry and molecular biology to explain structure formation in complex systems. These methods have been applied to many different fields such as economics, sociology and town planning, and this book reflects the interdisciplinary nature of complexity and self-organisation. The main focus is on the emergence of collective phenomena from individual or microscopic interactions. Presents a wide-ranging overview from fundamental aspects of the evolution of complexity, to applications in biology, ecology, sociology, economics, and urban structure formation.
The concept of self-organization is at the heart of the theory of complex systems. It describes how order can emerge from disorder in otherwise chaotic nonlinear dynamical systems. This book investigates and surveys the role of self-organization in a wide variety of disciplines. The contributions are written by world-renowned scientists and philosophers at a level that is accessible to nonspecialists.
Analyzes approaches to the study of complexity in the physical, biological, and social sciences.
The paradigm of self-organisation is fundamental to theories of collective action in economic science and democratic governance in political science. Self-organisation in these social systems critically depends on voluntary compliance with conventional rules: that is, rules which are made up, mutually agreed, and modifiable 'on the fly'. How, then, can we use the self-organisation observed in such social systems as an inspiration for decentralised computer systems, which can face similar problems of coordination, cooperation and collaboration between autonomous peers?Self-Organising Multi-Agent Systems presents an innovative and systematic approach to transforming theories of economics and politics (and elements of philosophy, psychology, and jurisprudence) into an executable logical specification of conventional rules. It shows how sets of such rules, called institutions, provide an algorithmic basis for designing and implementing cyber-physical systems, enabling intelligent software processes (called agents) to manage themselves in the face of competition for scarce resources. It also provides a basis for implementing socio-technical systems with interacting human and computational intelligences in a way that is sustainable, fair and legitimate.This interdisciplinary book is essential reading for anyone interested in the 'planned emergence' of global properties, commonly-shared values or successful collective action, especially as a product of social construction, knowledge management and political arrangements. For those studying both computer science and social sciences, this book offers a radically new gateway to a transformative understanding of complex system development and social system modelling.Understanding how a computational representation of qualitative values like justice and democracy can lead to stability and legitimacy of socio-technical systems is among the most pressing software engineering challenges of modern times. This book can be read as an invitation to make the Digital Society better.Related Link(s)
It is widely recognised that mainstream economics has failed to translate micro consistently into macro economics and to provide endogenous explanations for the continual changes in the economic system. Since the early 1980s, a growing number of economists have been trying to provide answers to these two key questions by applying an evolutionary approach. This new departure has yielded a rich literature with enormous variety, but the unifying principles connecting the various ideas and views presented are, as yet, not apparent. This 2005 volume brings together fifteen original articles from scholars - each of whom has made a significant contribution to the field - in their common effort to reconstruct economics as an evolutionary science. Using meso economics as an analytical entity to bridge micro and macro economics as well as static and dynamic realms, a unified economic theory emerges.
This invaluable book is the first of its kind on 'selforganizology', the science of self-organization. It covers a wide range of topics, such as the theory, principle and methodology of selforganizology, agent-based modelling, intelligence basis, ant colony optimization, fish/particle swarm optimization, cellular automata, spatial diffusion models, evolutionary algorithms, self-adaptation and control systems, self-organizing neural networks, catastrophe theory and methods, and self-organization of biological communities, etc.Readers will have an in-depth and comprehensive understanding of selforganizology, with detailed background information provided for those who wish to delve deeper into the subject and explore research literature.This book is a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences.