This book provides a theoretical background in computation to scientists who use computational methods. It explains how computing is used in the natural sciences, and provides a high-level overview of those aspects of computer science and software engineering that are most relevant for computational science. The focus is on concepts, results, and applications, rather than on proofs and derivations. The unique feature of this book is that it “connects the dots between computational science, the theory of computation and information, and software engineering. The book should help scientists to better understand how they use computers in their work, and to better understand how computers work. It is meant to compensate a bit for the general lack of any formal training in computer science and information theory. Readers will learn something they can use throughout their careers.
A New Discipline of Organizational, Entrepreneurial, and Social Innovation
Author: Benyamin B. Lichtenstein
Publisher: Oxford University Press (UK)
Category: Business & Economics
'Generative Emergence' provides insight into the non-linear dynamics that lead to organizational emergence through the use of complexity sciences. The book explores how the model of generative emergence could be applied to enact emergence within and across organizations.
Using Complexity Science to Theorise Organisational Aliveness
Author: Jacco van Uden
Category: Business & Economics
Students of organisation have used complexity theory in many different ways and for many different reasons. What characterises the writings of most 'management thinkers', however, is that the authors are primarily concerned with the question of "how to make this complexity thing work for us?" This study takes a rather different approach. Ideas and concepts of the science of complexity are borrowed to develop the idea that organisations live lives of their own - an idea that is very much at odds with the dominant view that understands organisations as tools that we use to realise certain goals. To illustrate matters, the book discusses the developments of the organisation of Vitesse, a mediocre Dutch professional football club that according to its president needed to be transformed into a major player in the family entertainment industry.
By now, most academics have heard something about the new science of complexity. In a manner reminiscent of Einstein and the last hundred years of physics, complexity science has captured the public imagination. ® One can go to Amazon. com and purchase books on complexification (Casti 1994), emergence (Holland 1998), small worlds (Barabási 2003), the web of life (Capra 1996), fuzzy thinking (Kosko 1993), global c- plexity (Urry 2003) and the business of long-tails (Anderson 2006). Even television has incorporated the topics of complexity science. Crime shows ® ® such as 24 or CSI typically feature investigators using the latest advances in computational modeling to “simulate scenarios” or “data mine” all p- sible suspects—all of which is done before the crime takes place. The ® World Wide Web is another example. A simple search on Google. Com using the phrase “complexity science” gets close to a million hits! C- plexity science is ubiquitous. What most scholars do not realize, however, is the remarkable role sociologists are playing in this new science. C- sider the following examples. 0. 1 Sociologists in Complexity Science The first example comes from the new science of networks (Barabási 2003). By now, most readers are familiar with the phenomena known as six-degrees of separation—the idea that, because most large networks are comprised of a significant number of non-random weak-ties, the nodes (e. g. , people, companies, etc.
An Introduction to Software Design - How to Think Like a Computer Scientist
Author: Allen B. Downey
Python for Software Design is a concise introduction to software design using the Python programming language. Intended for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practice each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises. ** Published under the terms of the GNU Free Documentation License. Money raised from the sale of this book supports the development of free software and documentation.
Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different point of view. While philosophers typically have an eye on general aspects of scientific modeling, scientists typically take their own science as the starting point and are often more concerned with specific methodological problems. There is, however, also much common ground in middle, where philosophers and scientists can engage in a productive dialogue, as the present volume demonstrates. To do so, the editors of this volume have invited eight leading scientists from cosmology, climate science, social science, chemical engeneering and neuroscience to reflect upon their modeling work, and eight philosophers of science to provide a commentary.
There are new and important advancements in todays complexity theories in ICT and requires an extraordinary perspective on the interaction between living systems and information technologies. With human evolution and its continuous link with the development of new tools and environmental changes, technological advancements are paving the way for new evolutionary steps. Complexity Science, Living Systems, and Reflexing Interfaces: New Models and Perspectives is a collection of research provided by academics and scholars aiming to introduce important advancements in areas such as artificial intelligence, evolutionary computation, neural networks, and much more. This scholarly piece will provide contributions that will define the line of development in complexity science.