The concepts of evolution and complexity theory have become part of the intellectual ether permeating the life sciences, the social and behavioral sciences, and, more recently, management science and economics. In this book, John E. Mayfield elegantly synthesizes core concepts from multiple disciplines to offer a new approach to understanding how evolution works and how complex organisms, structures, organizations, and social orders can and do arise based on information theory and computational science. Intended for the intellectually adventuresome, this book challenges and rewards readers with a nuanced understanding of evolution and complexity that offers consistent, durable, and coherent explanations for major aspects of our life experiences. Numerous examples throughout the book illustrate evolution and complexity formation in action and highlight the core function of computation lying at the work's heart.
Modern Humans is a vivid account of the most recent—and perhaps the most important—phase of human evolution: the appearance of anatomically modern people (Homo sapiens) in Africa less than half a million years ago and their later spread throughout the world. Leaving no stone unturned, John F. Hoffecker demonstrates that Homo sapiens represents a “major transition” in the evolution of living systems in terms of fundamental changes in the role of non-genetic information. Modern Humans synthesizes recent findings from genetics (including the rapidly growing body of ancient DNA), the human fossil record, and archaeology relating to the African origin and global dispersal of anatomically modern people. Hoffecker places humans in the broad context of the evolution of life, emphasizing the critical role of genetic and non-genetic forms of information in living systems as well as how changes in the storage, transmission, and translation of information underlie major transitions in evolution. He also draws on information and complexity theory to explain the emergence of Homo sapiens in Africa several hundred thousand years ago and the rapid and unprecedented spread of our species into a variety of environments in Australia and Eurasia, including the Arctic and Beringia, beginning between 75,000 and 60,000 years ago. This magisterial work will appeal to all with an interest in the ever-fascinating field of human evolution.
Genetic and Evolutionary Computation Conference Seattle, WA, USA, June 26–30, 2004, Proceedings
Author: Kalyanmoy Deb
The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.
The motivation behind this book is the desire to integrate complexity theory into economic models of technological evolution. By means of developing an evolutionary model of complex technological systems, the book contributes to the neo-Schumpetarian literature on innovation, diffusion and technological paradigms.
Economics is changing. In the last few years it has generated a number of new approaches. One of the most promising - complexity economics - was pioneered in the 1980s and 1990s by a small team at the Santa Fe Institute. Economist and complexity theorist W. Brian Arthur led that team, and in this book he collects many of his articles on this new approach. The traditional framework sees behavior in the economy as in an equilibrium steady state. People in the economy face well-defined problems and use perfect deductive reasoning to base their actions on. The complexity framework, by contrast, sees the economy as always in process, always changing. People try to make sense of the situations they face using whatever reasoning they have at hand, and together create outcomes they must individually react to anew. The resulting economy is not a well-ordered machine, but a complex evolving system that is imperfect, perpetually constructing itself anew, and brimming with vitality. The new vision complements and widens the standard one, and it helps answer many questions: Why does the stock market show moods and a psychology? Why do high-tech markets tend to lock in to the dominance of one or two very large players? How do economies form, and how do they continually alter in structure over time? The papers collected here were among the first to use evolutionary computation, agent-based modeling, and cognitive psychology. They cover topics as disparate as how markets form out of beliefs; how technology evolves over the long span of time; why systems and bureaucracies get more complicated as they evolve; and how financial crises can be foreseen and prevented in the future.
I think this is a very important book. Very few people in the social sciences write books on this topic and really do justice to complexity theory. Professor Room gives a very detailed, accurate and accessible review of complexity theory as it applies to social policy. His link with institutional theory is very appropriate and his discussion on the need for regulation (a link with complexity theory that many people would never reach) is really important and well grounded. It would be of interest to academics who really want to understand the implications of complexity theory for policy making in complex and fast-changing situations and to those undertaking advanced courses in politics, economics and sociology. - Jean Boulton, University of Cranfield, UK Graham Room argues that conventional approaches to the conceptualisation and measurement of social and economic change are unsatisfactory. As a result, researchers are ill-equipped to offer policy advice. This book offers a new analytical approach, combining complexity science and institutionalism. It also provides tools for policy makers in turbulent times. Part 1 is concerned with the conceptualisation of socio-economic change. It integrates complexity science and institutionalism into a coherent ontology of social and policy dynamics. Part 2 is concerned with models and measurement. It combines some of the principal approaches developed in complexity analysis with models and methods drawn from mainstream social and political science. Part 3 offers empirical applications to public policy: the dynamics of social exclusion; the social dimension of knowledge economies; the current financial and economic crisis. These are supplemented by a toolkit for the practice of agile policy making.
Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting from a single cell interacting with its environment, eventually including a changing local neighbourhood of other cells. These methods may help us understand the genesis, organization, adaptive plasticity, and evolvability of differentiated biological systems, and may also provide a paradigm for transferring these principles of biology's success to computational and engineering challenges at a scale not previously conceivable.
This book offers a definitive resource that bridges biology and evolutionary computation. The authors have written an introduction to biology and bioinformatics for computer scientists, plus an introduction to evolutionary computation for biologists and for computer scientists unfamiliar with these techniques.