Search Results: think-complexity-complexity-science-and-computational-modeling

Think Complexity

Complexity Science and Computational Modeling

Author: Allen Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1492040150

Category: Computers

Page: 200

View: 3411

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Think Complexity

Complexity Science and Computational Modeling

Author: Allen Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1449314635

Category: Computers

Page: 142

View: 5894

Enhances Python skills by working with data structures and algorithms and gives examples of complex systems using exercises, case studies, and simple explanations.

Think Complexity

Complexity Science and Computational Modeling

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1449331696

Category: Computers

Page: 160

View: 6589

Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations. You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise. Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get starter code and solutions to help you re-implement and extend original experiments in complexity Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics Examine case studies of complex systems submitted by students and readers

Think Bayes

Author: Allen Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1491945443

Category: Computers

Page: 210

View: 6625

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Think Stats

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1491907371

Category: Computers

Page: 226

View: 7136

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

Complexity Dichotomies for Counting Problems

Author: Jin-Yi Cai,Xi Chen

Publisher: Cambridge University Press

ISBN: 1107062373

Category: Computers

Page: 320

View: 4168

Volume 1. Boolean domain

Sociology and Complexity Science

A New Field of Inquiry

Author: Brian Castellani,Frederic William Hafferty

Publisher: Springer Science & Business Media

ISBN: 3540884629

Category: Science

Page: 277

View: 8062

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.

How to Think Like a Computer Scientist

C++ Version

Author: Allen B. Downey

Publisher: CreateSpace

ISBN: 9781441419057

Category: Computers

Page: 192

View: 6739

HOW TO THINK LIKE A COMPUTER SCIENTIST. * 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.

Thinking in Complexity

The Complex Dynamics of Matter, Mind, and Mankind

Author: Klaus Mainzer

Publisher: Springer Science & Business Media

ISBN: 3662033054

Category: Science

Page: 351

View: 7723

Since the first edition sold out in less than a year, we now present the revised second edition of Mainzer's popular book. The theory of nonlinear complex systems has become a successful problem-solving approach in the natural sciences from laser physics, quantum chaos, and meteorology to computer simulations of cell growth in biology. It is now recognized that many of our social, ecological, and political problems are also of a global, complex, and nonlinear nature. And one of the most exciting contemporary topics is the idea that even the human mind is governed largely by the nonlinear dynamics of complex systems. In this wide-ranging but concise treatment, Prof. Mainzer discusses, in a nontechnical language, the common framework behind these endeavors. Emphasis is given to the evolution of new structures in natural and cultural systems and we see clearly how the new integrative approach can give insights not available from traditional reductionistic methods.

Think Python

How to Think Like a Computer Scientist

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1491939419

Category: Computers

Page: 292

View: 2917

If you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand objects, methods, and object-oriented programming Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design, data structures, and GUI-based programs through case studies

Think Java

How to Think Like a Computer Scientist

Author: Allen B. Downey,Chris Mayfield

Publisher: "O'Reilly Media, Inc."

ISBN: 1491929537

Category: Computers

Page: 252

View: 9755

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned. Learn one concept at a time: tackle complex topics in a series of small steps with examples Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately Determine which development techniques work best for you, and practice the important skill of debugging Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays Work on exercises involving word games, graphics, puzzles, and playing cards

Think DSP

Digital Signal Processing in Python

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 149193851X

Category: Technology & Engineering

Page: 168

View: 6609

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.

Diversity and Complexity

Author: Scott E. Page

Publisher: Princeton University Press

ISBN: 9781400835140

Category: Political Science

Page: 304

View: 7000

This book provides an introduction to the role of diversity in complex adaptive systems. A complex system--such as an economy or a tropical ecosystem--consists of interacting adaptive entities that produce dynamic patterns and structures. Diversity plays a different role in a complex system than it does in an equilibrium system, where it often merely produces variation around the mean for performance measures. In complex adaptive systems, diversity makes fundamental contributions to system performance. Scott Page gives a concise primer on how diversity happens, how it is maintained, and how it affects complex systems. He explains how diversity underpins system level robustness, allowing for multiple responses to external shocks and internal adaptations; how it provides the seeds for large events by creating outliers that fuel tipping points; and how it drives novelty and innovation. Page looks at the different kinds of diversity--variations within and across types, and distinct community compositions and interaction structures--and covers the evolution of diversity within complex systems and the factors that determine the amount of maintained diversity within a system. Provides a concise and accessible introduction Shows how diversity underpins robustness and fuels tipping points Covers all types of diversity The essential primer on diversity in complex adaptive systems

Complexity and Real Computation

Author: Lenore Blum,Felipe Cucker,Michael Shub,Steve Smale

Publisher: Springer Science & Business Media

ISBN: 1461207010

Category: Computers

Page: 453

View: 8201

The classical theory of computation has its origins in the work of Goedel, Turing, Church, and Kleene and has been an extraordinarily successful framework for theoretical computer science. The thesis of this book, however, is that it provides an inadequate foundation for modern scientific computation where most of the algorithms are real number algorithms. The goal of this book is to develop a formal theory of computation which integrates major themes of the classical theory and which is more directly applicable to problems in mathematics, numerical analysis, and scientific computing. Along the way, the authors consider such fundamental problems as: * Is the Mandelbrot set decidable? * For simple quadratic maps, is the Julia set a halting set? * What is the real complexity of Newton's method? * Is there an algorithm for deciding the knapsack problem in a ploynomial number of steps? * Is the Hilbert Nullstellensatz intractable? * Is the problem of locating a real zero of a degree four polynomial intractable? * Is linear programming tractable over the reals? The book is divided into three parts: The first part provides an extensive introduction and then proves the fundamental NP-completeness theorems of Cook-Karp and their extensions to more general number fields as the real and complex numbers. The later parts of the book develop a formal theory of computation which integrates major themes of the classical theory and which is more directly applicable to problems in mathematics, numerical analysis, and scientific computing.

Introduction to Computation and Programming Using Python

With Application to Understanding Data

Author: John V. Guttag

Publisher: MIT Press

ISBN: 0262529629

Category: Computers

Page: 472

View: 1933

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.

Complexity and the Human Experience

Modeling Complexity in the Humanities and Social Sciences

Author: Paul A. Youngman,Mirsad Hadzikadic

Publisher: CRC Press

ISBN: 9814463272

Category: Social Science

Page: 304

View: 6771

Questions of values, ontologies, ethics, aesthetics, discourse, origins, language, literature, and meaning do not lend themselves readily, or traditionally, to equations, probabilities, and models. However, with the increased adoption of natural science tools in economics, anthropology, and political science—to name only a few social scientific fields highlighted in this volume—quantitative methods in the humanities are becoming more common. The theory of complexity holds significant promise for better understanding social and human phenomena based on interactions among the participating "agents," whatever they may be: a thought, a person, a conversation, a sentence, or an email. Such systems can exhibit phase transitions, feedback loops, self-organization, and emergent properties. These dynamic systems lend themselves naturally to the kind of analysis made possible by models and simulations developed with complex science tools. This volume offers a tour of quantitative analyses, models, and simulations of humanities and social science phenomena that have been historically the purview of qualitative methods.

wxPython Recipes

A Problem - Solution Approach

Author: Mike Driscoll

Publisher: Apress

ISBN: 1484232372

Category: Computers

Page: 348

View: 3148

Quickly discover solutions to common problems, learn best practices, and understand everything wxPython has to offer. This book is for anyone wanting to learn more about how to use the wxPython desktop GUI toolkit. It assumes some prior knowledge of Python and a general understanding of wxPython or GUI development, and contains more than 50 recipes covering various tasks and aspects of the toolkit. wxPython Recipes guides you step by step. The book takes you through how to create user interfaces in Python, including adding widgets, changing background images, manipulating dialogs, managing data, and much more. Examples target both Python 2.x and 3.x, and cover both wxPython 3.0 and Phoenix, offering a complete collection of ideas to improve your GUI development. What You'll Learn Work with UI elements such as widgets, buttons, images, boxes, and more Handle data in files and notebooks Implement XML and using XML resources (XRC) Customize the behavior of panels and objects Who This Book Is For People who are already familiar with the Python programming language and also have a basic understanding of wxPython. Readers who understand event loops and the basics of creating user interfaces with another Python UI toolkit, such as Tkinter or PyQt.

Computational Complexity

A Conceptual Perspective

Author: Oded Goldreich

Publisher: Cambridge University Press

ISBN: 1139472747

Category: Computers

Page: N.A

View: 2662

Complexity theory is a central field of the theoretical foundations of computer science. It is concerned with the general study of the intrinsic complexity of computational tasks; that is, it addresses the question of what can be achieved within limited time (and/or with other limited natural computational resources). This book offers a conceptual perspective on complexity theory. It is intended to serve as an introduction for advanced undergraduate and graduate students, either as a textbook or for self-study. The book will also be useful to experts, since it provides expositions of the various sub-areas of complexity theory such as hardness amplification, pseudorandomness and probabilistic proof systems. In each case, the author starts by posing the intuitive questions that are addressed by the sub-area and then discusses the choices made in the actual formulation of these questions, the approaches that lead to the answers, and the ideas that are embedded in these answers.

Complex Adaptive Systems

An Introduction to Computational Models of Social Life

Author: John H. Miller,Scott E. Page

Publisher: Princeton University Press

ISBN: 9781400835522

Category: Social Science

Page: 288

View: 8215

This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.


Theory and Applications

Author: Nam P. Suh

Publisher: Oxford University Press on Demand

ISBN: 0195178769

Category: Technology & Engineering

Page: 300

View: 1479

Nam P. Suh focussed his axiomatic design theories on methods to understand and deal with complexity. Suh is a well-respected designer and researcher in the fields of manufacturing and composite materials. He is best known for his systems that aim to speed up and simplify the process of design for manufacturing. The 'axioms' in axiomatic design refer to a process to help engineers reduce design specifications down to their simplest components, so that the engineers can produce the simplest possible solution to a problem. Complexity, besides being a key area of burgeoning research in disciplines interested in complex systems and chaos theory (like computer science and physics), is a complicating factor in engineering design that many engineers find difficult to overcome. Suh's multidisciplinary exploration of complex systems is meant to eliminate much of the confusion and allow engineers to accommodate complexity within simple, elegant design solutions.

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