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Analyzes approaches to the study of complexity in the physical, biological, and social sciences.
Complexity is emerging as a post-Newtonian paradigm for approaching a large body of phenomena of concern at the crossroads of physical, engineering, environmental, life and human sciences from a unifying point of view. This book outlines the foundations of modern complexity research as it arose from the cross-fertilization of ideas and tools from nonlinear science, statistical physics and numerical simulation. It is shown how these developments lead to an understanding, both qualitative and quantitative, of the complex systems encountered in nature and in everyday experience and, conversely, how natural complexity acts as a source of inspiration for progress at the fundamental level.
This volume explores the universal mathematical properties underlying big language data and possible reasons why such properties exist, revealing how we may be unconsciously mathematical in our language use. These properties are statistical and thus different from linguistic universals that contribute to describing the variation of human languages, and they can only be identified over a large accumulation of usages. The book provides an overview of state-of-the art findings on these statistical universals and reconsiders the nature of language accordingly, with Zipf's law as a well-known example. The main focus of the book further lies in explaining the property of long memory, which was discovered and studied more recently by borrowing concepts from complex systems theory. The statistical universals not only possibly lie as the precursor of language system formation, but they also highlight the qualities of language that remain weak points in today's machine learning. In summary, this book provides an overview of language's global properties. It will be of interest to anyone engaged in fields related to language and computing or statistical analysis methods, with an emphasis on researchers and students in computational linguistics and natural language processing. While the book does apply mathematical concepts, all possible effort has been made to speak to a non-mathematical audience as well by communicating mathematical content intuitively, with concise examples taken from real texts.
Interdisciplinary perspectives on the evolutionary and biological roots of syntax, describing current research on syntax in fields ranging from linguistics to neurology. Syntax is arguably the most human-specific aspect of language. Despite the proto-linguistic capacities of some animals, syntax appears to be the last major evolutionary transition in humans that has some genetic basis. Yet what are the elements to a scenario that can explain such a transition? In this book, experts from linguistics, neurology and neurobiology, cognitive psychology, ecology and evolutionary biology, and computer modeling address this question. Unlike most previous work on the evolution of language, Biological Foundations and Origin of Syntax follows through on a growing consensus among researchers that language can be profitably separated into a number of related and interacting but largely autonomous functions, each of which may have a distinguishable evolutionary history and neurological base. The contributors argue that syntax is such a function.The book describes the current state of research on syntax in different fields, with special emphasis on areas in which the findings of particular disciplines might shed light on problems faced by other disciplines. It defines areas where consensus has been established with regard to the nature, infrastructure, and evolution of the syntax of natural languages; summarizes and evaluates contrasting approaches in areas that remain controversial; and suggests lines for future research to resolve at least some of these disputed issues. Contributors Andrea Baronchelli, Derek Bickerton, Dorothy V. M. Bishop, Denis Bouchard, Robert Boyd, Jens Brauer, Ted Briscoe, David Caplan, Nick Chater, Morten H. Christiansen, Terrence W.Deacon, Francesco d'Errico, Anna Fedor, Julia Fischer, Angela D. Friederici, Tom Givón, Thomas Griffiths, Balázs Gulyás, Peter Hagoort, Austin Hilliard, James R. Hurford, Péter Ittzés, Gerhard Jäger, Herbert Jäger, Edith Kaan, Simon Kirby, Natalia L. Komarova, Tatjana Nazir, Frederick Newmeyer, Kazuo Okanoya, Csaba Plèh, Peter J. Richerson, Luigi Rizzi, Wolf Singer, Mark Steedman, Luc Steels, Szabolcs Számadó, Eörs Szathmáry, Maggie Tallerman, Jochen Triesch, Stephanie Ann White
This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.
Physicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the `best' inference. But are these methods equivalent, or not? What is the state of the art in making inferences? The physicists want answers. More: neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Matter enough to stimulate the writing of such a book as the present one. But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This lively debate, which is of acute current interest, is well summarized in the present work.
The proceedings consist of four lectures which give a general review as well as discuss some of the recent advances on the topics listed.
In Complexity and Postmodernism, Paul Cilliers explores the idea of complexity in the light of contemporary perspectives from philosophy and science. Cilliers offers us a unique approach to understanding complexity and computational theory by integrating postmodern theory (like that of Derrida and Lyotard) into his discussion. Complexity and Postmodernism is an exciting and an original book that should be read by anyone interested in gaining a fresh understanding of complexity, postmodernism and connectionism.