Content Complexity

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.

Complexity theory has become popular in the natural and social sciences over the last few decades as a result of the advancements in our understanding of the complexities in natural and social phenomena. Concepts and methods of complexity theory have been applied by scholars of public affairs in North America and Europe, but a comprehensive framework for these applications is lacking. A Complexity Theory for Public Policy proposes a conceptual synthesis and sets a foundation for future developments and applications. In this book, Göktuğ Morçöl convincingly makes the case that complexity theory can help us understand better the self-organizational, emergent, and co-evolutionary characteristics of complex policy systems. In doing so, he discuss the epistemological implications of complexity theory and the methods complexity researchers use, and those methods they could use. As the complexity studies spread more around the world in the coming decades, the contents of this book will become appealing to larger audiences, particularly to scholars and graduate students in public affairs. The unique combination of synthesis and explanation of concepts and methods found in this book will serve as reference frames for future works.

The book is a collection of papers written by a selection of eminent authors from around the world in honour of Gregory Chaitin's 60th birthday. This is a unique volume including technical contributions, philosophical papers and essays.

“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

This book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance and applicability. Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently coexist for the first time, ranging from the dominant ones based upon popular statistical lossless compression algorithms (such as LZW) to newer approaches that advance, complement, and also pose their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented, and despite their many challenges, some of these methods are better grounded in or motivated by the principles of algorithmic information. The authors propose that the field can make greater contributions to science, causation, scientific discovery, networks, and cognition, to mention a few among many fields, instead of remaining either as a technical curiosity of mathematical interest only or as a statistical tool when collapsed into an application of popular lossless compression algorithms. This book goes, thus, beyond popular statistical lossless compression and introduces a different methodological approach to dealing with algorithmic complexity. For example, graph theory and network science are classic subjects in mathematics widely investigated in the twentieth century, transforming research in many fields of science from economy to medicine. However, it has become increasingly clear that the challenge of analyzing these networks cannot be addressed by tools relying solely on statistical methods. Therefore, model-driven approaches are needed. Recent advances in network science suggest that algorithmic information theory could play an increasingly important role in breaking those limits imposed by traditional statistical analysis (entropy or statistical compression) in modeling evolving complex networks or interacting networks. Further progress on this front calls for new techniques for an improved mechanistic understanding of complex systems, thereby calling out for increased interaction between systems science, network theory, and algorithmic information theory, to which this book contributes.

This book provides a mosaic of perspectives leading to a better understanding of conceptual and practical approaches to integrative learning and teaching.

There is a big difference between assigning complex texts and teaching complex texts No matter what discipline you teach, learn how to use complexity as a dynamic, powerful tool for sliding the right text in front of your students’ at just the right time. Updates to this new edition include How-to’s for measuring countable features of any written work A rubric for analyzing the complexity of both literary and informational texts Classroom scenarios that show the difference between a healthy struggle and frustration The authors’ latest thinking on teacher modeling, close reading, scaffolded small group reading, and independent reading

This book constitutes the refereed proceedings of the First International Conference on Advanced Data Mining and Applications, ADMA 2005, held in Wuhan, China in July 2005. The conference was focused on sophisticated techniques and tools that can handle new fields of data mining, e.g. spatial data mining, biomedical data mining, and mining on high-speed and time-variant data streams; an expansion of data mining to new applications is also strived for. The 25 revised full papers and 75 revised short papers presented were carefully peer-reviewed and selected from over 600 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.