In the context of this book, adaptation is taken to mean a feature of a system aimed at achieving the best possible performance, when mathematical models of the environment and the system itself are not fully available. This has applications ranging from theories of visual perception and the processing of information, to the more technical problems of friction compensation and adaptive classification of signals in fixed-weight recurrent neural networks. Largely devoted to the problems of adaptive regulation, tracking and identification, this book presents a unifying system-theoretic view on the problem of adaptation in dynamical systems. Special attention is given to systems with nonlinearly parameterized models of uncertainty. Concepts, methods and algorithms given in the text can be successfully employed in wider areas of science and technology. The detailed examples and background information make this book suitable for a wide range of researchers and graduates in cybernetics, mathematical modelling and neuroscience.
"The emphasis on change at many levels of organization is critically important as is the first attempt to integrate sophisticated theory and research in organization psychology (e.g., Gersick, Hackman) with social psychological models of development such as Moreland and Levine." --Reuben M. Baron, Emeritus, University of Connecticut "Arrow, McGrath, and Berdahl′s ′Small Groups as Complex Systems′ will change the way you think about groups, the way you think about research, and even the way you think about science." --Donelson R. Forsyth, Psychology, Virginia Commonwealth U "The book is excellent, one of those very rare works that will have substantial impact on the field. I would use the book without hesitation in any advanced graduate seminar dealing with groups." --Donelson R. Forsyth, Psychology, Virginia Commonwealth U "A conceptually elegant analysis of groups as systems. Although the systems approach has been growing more influential in various fields of social psychology in the last ten years, no one has put forward a definitive analysis that applies with fidelity the general systems approach to group processes. McGrath and his colleagues fill that gap, not by paying lip service to popular scientific concepts such as recursive causality, open systems, attractors, and complexity theory, but by fully integrating these concepts into their no-nonsense analysis of such group level processes as formation, task performance, composition, development, and termination. Empirical work is folded into the theoretical mix along the way, but the focus is unrelentingly conceptual with the result that the authors deliver on their promise of developing a powerful, unified theory of group dynamics." --Donelson R. Forsyth, Psychology, Virginia Commonwealth U "Theirs is an ambitious book. They have profound ramifications for experimental social psychology. It is worth mentioning that AMD (Arrow, McGrarth, and Berdahl) list an ethnographic approach, which often implies the adoption of hermeneutic and semiotic methods (a hallmark of the anti-Enlightenment tradition in psychology), as a possible way forward." --Yoshihisa Kashima, American Journal of Psychology What are groups? How do they behave? Arrow, McGrath, and Berdahl answer these questions by developing a general theory of small groups as complex systems. Basing their theory on concepts distilled from general systems theory, dynamical systems theory, and complexity and chaos theory, they explore groups as adaptive, dynamic systems that are driven by interactions among group members as well as between the group and its embedding contexts. In addition, they consider not only the group′s members and their distribution of attributes, but also the group′s tasks and technology in order to understand how those members, tasks, and tools are intertwined, coordinated, and adjusted. Throughout the book, the authors focus our attention on relationships among people, tools, and tasks that are activated by a combination of individual and collective purposes and goals that change and evolve as the group interacts over time.
“If you liked Chaos, you’ll love Complexity. Waldrop creates the most exciting intellectual adventure story of the year” (The Washington Post). In a rarified world of scientific research, a revolution has been brewing. Its activists are not anarchists, but rather Nobel Laureates in physics and economics and pony-tailed graduates, mathematicians, and computer scientists from all over the world. They have formed an iconoclastic think-tank and their radical idea is to create a new science: complexity. They want to know how a primordial soup of simple molecules managed to turn itself into the first living cell—and what the origin of life some four billion years ago can tell us about the process of technological innovation today. This book is their story—the story of how they have tried to forge what they like to call the science of the twenty-first century. “Lucidly shows physicists, biologists, computer scientists and economists swapping metaphors and reveling in the sense that epochal discoveries are just around the corner . . . [Waldrop] has a special talent for relaying the exhilaration of moments of intellectual insight.” —The New York Times Book Review “Where I enjoyed the book was when it dove into the actual question of complexity, talking about complex systems in economics, biology, genetics, computer modeling, and so on. Snippets of rare beauty here and there almost took your breath away.” —Medium “[Waldrop] provides a good grounding of what may indeed be the first flowering of a new science.” —Publishers Weekly
How we produce and consume food has a bigger impact on Americans' well-being than any other human activity. The food industry is the largest sector of our economy; food touches everything from our health to the environment, climate change, economic inequality, and the federal budget. From the earliest developments of agriculture, a major goal has been to attain sufficient foods that provide the energy and the nutrients needed for a healthy, active life. Over time, food production, processing, marketing, and consumption have evolved and become highly complex. The challenges of improving the food system in the 21st century will require systemic approaches that take full account of social, economic, ecological, and evolutionary factors. Policy or business interventions involving a segment of the food system often have consequences beyond the original issue the intervention was meant to address. A Framework for Assessing Effects of the Food System develops an analytical framework for assessing effects associated with the ways in which food is grown, processed, distributed, marketed, retailed, and consumed in the United States. The framework will allow users to recognize effects across the full food system, consider all domains and dimensions of effects, account for systems dynamics and complexities, and choose appropriate methods for analysis. This report provides example applications of the framework based on complex questions that are currently under debate: consumption of a healthy and safe diet, food security, animal welfare, and preserving the environment and its resources. A Framework for Assessing Effects of the Food System describes the U.S. food system and provides a brief history of its evolution into the current system. This report identifies some of the real and potential implications of the current system in terms of its health, environmental, and socioeconomic effects along with a sense for the complexities of the system, potential metrics, and some of the data needs that are required to assess the effects. The overview of the food system and the framework described in this report will be an essential resource for decision makers, researchers, and others to examine the possible impacts of alternative policies or agricultural or food processing practices.
This book introduces a generic approach to model the use and adaptation of mental models, including the control over this. In their mental processes, humans often make use of internal mental models as a kind of blueprints for processes that can take place in the world or in other persons. By internal mental simulation of such a mental model in their brain, they can predict and be prepared for what can happen in the future. Usually, mental models are adaptive: they can be learned, refined, revised, or forgotten, for example. Although there is a huge literature on mental models in various disciplines, a systematic account of how to model them computationally in a transparent manner is lacking. This approach allows for computational modeling of humans using mental models without a need for any algorithmic or programming skills, allowing for focus on the process of conceptualizing, modeling, and simulating complex, real-world mental processes and behaviors. The book is suitable for and is used as course material for multidisciplinary Master and Ph.D. students.
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
This comprehensive Handbook is aimed at both academic researchers and practitioners in the field of complexity science. The book’s 26 chapters, specially written by leading experts, provide in-depth coverage of research methods based on the sciences of complexity. The research methods presented are illustratively applied to practical cases and are readily accessible to researchers and decision makers alike.
A novel, integrated approach to understanding long-term human history, viewing it as the long-term evolution of human information-processing. This title is also available as Open Access.
