The book is a collection of papers of experts in the fields of information and complexity. Information is a basic structure of the world, while complexity is a fundamental property of systems and processes. There are intrinsic relations between information and complexity.The research in information theory, the theory of complexity and their interrelations is very active. The book will expand knowledge on information, complexity and their relations representing the most recent and advanced studies and achievements in this area.The goal of the book is to present the topic from different perspectives — mathematical, informational, philosophical, methodological, etc.
The twin themes of computational complexity and information pervade this 1998 book. It starts with an introduction to the computational complexity of continuous mathematical models, that is, information-based complexity. This is then used to illustrate a variety of topics, including breaking the curse of dimensionality, complexity of path integration, solvability of ill-posed problems, the value of information in computation, assigning values to mathematical hypotheses, and new, improved methods for mathematical finance. The style is informal, and the goals are exposition, insight and motivation. A comprehensive bibliography is provided, to which readers are referred for precise statements of results and their proofs. As the first introductory book on the subject it will be invaluable as a guide to the area for the many students and researchers whose disciplines, ranging from physics to finance, are influenced by the computational complexity of continuous problems.
This book provides a comprehensive treatment of information-based complexity, the branch of computational complexity that deals with the intrinsic difficulty of the approximate solution of problems for which the information is partial, noisy, and priced. Such problems arise in many areas including economics, physics, human and robotic vision, scientific and engineering computation, geophysics, decision theory, signal processing and control theory.
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
This book is about complexity in Information Systems (IS). The subject is addressed from both conceptual and applied perspectives. Concepts are drawn from information theory, industrial design and software engineering. Its content capitalizes on experiences gathered by the authors during various contracting jobs involving software architecture, modeling and IS architecture that were conducted for large organizations in the banking and industry sectors, as well as in the public sector. The authors develop the point of view according to which mastering complexity involves two essential steps: first, one should develop a clear understanding of the real nature of complexity within the IS; second, one should identify the primary causes which contribute to its uncontrolled growth and organize these into a logical framework, in order to define efficient countermeasures. Both technical and psychological causes of complexity are to be considered. Two themes make up the main thread of the book: complexity and value. Both themes are quite common when considered separately, but their interplay remains a largely unexplored topic. The analysis of this interplay is one of the sources of originality of this book.
Information design is an emerging area in technical communication, garnering increased attention in recent times as more information is presented through both old and new media. In this volume, editors Michael J. Albers and Beth Mazur bring together scholars and practitioners to explore the issues facing those in this exciting new field. Treating information as it applies to technical communication, with a special emphasis on computer-centric industries, this volume delves into the role of information design in assisting with concepts, such as usability, documenting procedures, and designing for users. Influential members in the technical communication field examine such issues as the application of information design in structuring technical material; innovative ways of integrating information design within development methodologies and social aspects of the workplace; and theoretical approaches that include a practical application of information design, emphasizing the intersection of information design theories and workplace reality. This collection approaches information design from the language-based technical communication side, emphasizing the role of content as it relates to complexity in information design. As such, it treats as paramount the rhetorical and contextual strategies required for the effective design and transmission of information. Content and Complexity: Information Design in Technical Communication explores both theoretical perspectives, as well as the practicalities of information design in areas relevant to technical communicators. This integration of theoretical and applied components make it a practical resource for students, educators, academic researchers, and practitioners in the technical communication and information design fields.
Manuel Lima's smash hit Visual Complexity is now available in paperback. This groundbreaking 2011 book—the first to combine a thorough history of information visualization with a detailed look at today's most innovative applications—clearly illustrates why making meaningful connections inside complex data networks has emerged as one of the biggest challenges in twenty-first-century design. From diagramming networks of friends on Facebook to depicting interactions among proteins in a human cell, Visual Complexity presents one hundred of the most interesting examples of informationvisualization by the field's leading practitioners.
Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).
New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
