Santa Fe Institute Studies on the Sciences of Complexity – serie

Scaling relationships have been a persistent theme in biology at least since the time of Leonardo da Vinci and Galileo. Because scaling relationships are among the most general empirical patterns in biology, they have stimulated research to develop mechanistic hypotheses and mathematical models. While there have been many excellent empirical and theoretical investigations, there has been little attempt to synthesize this diverse but interrelated area of biology. In an effort to fill this void, Scaling in Biology, the first general treatment of scaling in biology in over 15 years, covers a broad spectrum of the most relevant topics in a series of chapters written by experts in the field. Some of those topics discussed include allometry and fractal structure, branching of vascular systems of mammals and plants, biomechanical and life history of plants, invertebrates and vertebrates, and species-area patterns of biological diversity. Many more examples are included within this text to complete the broader picture. Scaling in Biology conveys the diversity, promise, and excitement of current research in this area, in a format accessible to a wide audience of not only specialists in the various sub-disciplines, but also students and anyone with a serious interest in biology.

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behaviour and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behaviour and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

As part of the SFI series, this book presents the most up-to-date research in the study of human and primate societies, presenting recent advances in software and algorithms for modelling societies. It also addresses case studies that have applied agent-based modelling approaches in archaeology, cultural anthropology, primatology, and sociology. Many things set this book apart from any other on modelling in the social sciences, including the emphasis on small-scale societies and the attempts to maximize realism in the modelling efforts applied to social problems and questions. It is an ideal book for professionals in archaeology or cultural anthropology as well as a valuable tool for those studying primatology or computer science.

This book not only discusses cellular automata (CA) as accouterment for simulation, but also the actual building of devices within cellular automata. CA are widely used tools for simulation in physics, ecology, mathematics, and other fields. But they are also digital "toy universes" worthy of study in their own right, with their own laws of physics and behavior. In studying CA for their own sake, we must look at constructive methods, that is the practice of actually building devices in a given CA that store and process in formation, replicate, and propagate themselves, and interact with other devices in complex ways. By building such machines, we learn what the CA's dynamics are capable of, and build an intuition about how to "engineer" the machine we want. We can also address fundamental questions, such as whether universal computation or even "living" things that reproduce and evolve can exist in the CA's digital world, and perhaps, how these things came to be in out own universe.

For those addressing ecological and natural resource management problems this volume presents a set of coherent, cross-referenced perspectives on incorporating the spatial representation and analytical power of GIS with agent-based modeling of evolutionary and non-linear processes and phenomena. Many recent advances in software algorithms for incorporating geographic data in modeling social and ecological behaviors and also the success in applying such algorithms have not been adequately represented in the present literature. This book fills that gap and provides much needed information on applications for the research community as well as those in the management of natural resources.

For those addressing ecological and natural resource management problems this volume presents a set of coherent, cross-referenced perspectives on incorporating the spatial representation and analytical power of GIS with agent-based modeling of evolutionary and non-linear processes and phenomena. Many recent advances in software algorithms for incorporating geographic data in modeling social and ecological behaviors and also the success in applying such algorithms have not been adequately written about This book fills that gap and provides much needed information on applications for the research community as well as those in the management of natural resources.

The Internet may be viewed as a "complex system" with diverse features and many components that can give rise to unexpected emergent phenomena, revealing much about its own engineering. This book brings together chapter contributions from a workshop held at the Santa Fe Institute in March 2001. This volume captures a snapshot of some features of the Internet that may be fruitfully approached using a complex systems perspective, meaning using interdisciplinary tools and methods to tackle the subject area. The Internet penetrates the socioeconomic fabric of everyday life; a broader and deeper grasp of the Internet may be needed to meet the challenges facing the future.The resulting empirical data have already proven to be invaluable for gaining novel insights into the network's spatio-temporal dynamics, and can be expected to become even more important when trying to explain the Internet's complex and emergent behaviour in terms of elementary networking-based mechanisms. The discoveries of fractal or self-similar network traffic traces, power-law behaviour in network topology and World Wide Web connectivity are instances of unsuspected, emergent system traits. Another important factor at the heart of fair, efficient, and stable sharing of network resources is user behaviour. Network systems, when habited by selfish or greedy users, take on the traits of a non-cooperative multi-party game, and their stability and efficiency are integral to understanding the overall system and its dynamics. Lastly, fault-tolerance and robustness of large-scale network systems can exhibit spatial and temporal correlations whose effective analysis and management may benefit from rescaling techniques applied in certain physical and biological systems. The present book will bring together several of the leading workers involved in the analysis of complex problems with the future development of the Internet.

The Internet may be viewed as a "complex system" with diverse features and many components that can give rise to unexpected emergent phenomena, revealing much about its own engineering. This book brings together chapter contributions from a workshop held at the Santa Fe Institute in March 2001. This volume captures a snapshot of some features of the Internet that may be fruitfully approached using a complex systems perspective, meaning using interdisciplinary tools and methods to tackle the subject area. The Internet penetrates the socioeconomic fabric of everyday life; a broader and deeper grasp of the Internet may be needed to meet the challenges facing the future. The resulting empirical data have already proven to be invaluable for gaining novel insights into the network's spatio-temporal dynamics, and can be expected to become even more important when tryin to explain the Internet's complex and emergent behavior in terms of elementary networking-based mechanisms. The discoveries of fractal or self-similar network traffic traces, power-law behavior in network topology and World Wide Web connectivity are instances of unsuspected, emergent system traits. Another important factor at the heart of fair, efficient, and stable sharing of network resources is user behavior. Network systems, when habited by selfish or greedy users, take on the traits of a noncooperative multi-party game, and their stability and efficiency are integral to understanding the overall system and its dynamics. Lastly, fault-tolerance and robustness of large-scale network systems can exhibit spatial and temporal correlations whose effective analysis and management may benefit from rescaling techniques applied in certain physical and biological systems. The present book will bring together several of the leading workers involved in the analysis of complex systems with the future development of the Internet.

Developed after a meeting at the Santa Fe Institute on extinction modeling, this book comments critically on the various modeling approaches. In the last decade or so, scientists have started to examine a new approach to the patterns of evolution and extinction in the fossil record. This approach may be called "statistical paleontology," since it looks at large-scale patterns in the record and attempts to understand and model their average statistical features, rather than their detailed structure. Examples of the patterns these studies examine are the distribution of the sizes of mass extinction events over time, the distribution of species lifetimes, or the apparent increase in the number of species alive over the last half a billion years. In attempting to model these patterns, researchers have drawn on ideas not only from paleontology, but from evolutionary biology, ecology, physics, and applied mathematics, including fitness landscapes, competitive exclusion, interaction matrices, and self-organized criticality. A self-contained review of work in this field.

A great variety of complex phenomena in many scientific fields exhibit power-law behaviour, reflecting a hierarchical or fractal structure. Many of these phenomena seem to be susceptible to description using approaches drawn from thermodynamics or statistical mechanics, particularly approaches involving the maximization of entropy. During recent years a good deal of study has been devoted to a nonextensive generalizations of entropy and of Boltzmann-Gibbs statistical mechanics and standard laws in a natural way. The book addresses the interdisciplinary applications of these ideas, and also on various phenomena that could possibly be quantitatively describable in terms of these ideas.

Derived from the 2001 Santa Fe Institute Conference, "The Economy as an Evolving Complex System III," represents scholarship from the leading figures in th area of economics and complexity. The subject, a perennial centerpiece of the SFI program of studies has gained a wide range of followers for its methods of employing empirical evidence in the development of analytical economic theories. Accordingly, the chapters in this volume addresses a wide variety of issues in the fields of economics and complexity, accessing eclectic techniques from many disciplines, provided that they shed light on the economic problem. Dedicated to Kenneth Arrow on his 80th birthday, this volume honors his many contributions to the Institute. SFI-style economics is regarded as having had an important impact in introducing a new approach to economic analysis.

This book consists of 17 papers on the contributions of John Holland by a distinguished group of scholars from a wide range of fields, including the Nobel laureates Kenneth Arrow and Herbert Simon, and also Douglas Hofstadter, Brian Arthur, Robert Axelrod, and Melanie Mitchell. Holland is the father of genetic algorithms, the pioneering worker exploring "emergence," and one of the leaders of the sciences of complexity. Holland's work is especially important for economics and evolutionary biology, artificial intelligence and machine learning, artificial life, and cognitive science.

Robust Design brings together 16 chapters by an eminent group of authors in a wide range of fields presenting aspects of robustness in biological, ecological, and computational systems. The volme is the first to address robustness in biological, ecological, and computational systems. It is an outgrowth of a new research program on robustness at the Sante Fe Institute founded by the David and Lucile Packard Foundation. For those interested in complexity or interdisciplinary science, robustness is seen as currently among the most intellectually active and promising research areas with important applications in all fields of science, business, and economics.

Robust Design brings together 16 chapters by an eminent group of authors in a wide range of fields presenting aspects of robustness in biological, ecological, and computational systems. The volume is the first to address robustness in biological, ecological, and computational systems. It is an outgrowth of a new research program on robustness at the Sante Fe Institute founded by the David and Lucile Packard Foundation. For those interested in complexity or interdisciplinary science, robustness is seen as currently among the most intellectually active and promising research areas with important applications in all fields of science, business, and economics.

Computer science and physics have been closely linked since the birth of modern computing. In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges. Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring. This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them. Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems. Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists. It will prepare students and researchers from all of these fields to contribute to this exciting area.

Computer science and physics have been closely linked since the birth of modern computing. In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges. Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring. This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them. Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems. Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists. It will prepare students and researchers from all of these fields to contribute to this exciting area.