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Products related to Complexity:


  • Think Complexity : Complexity Science and Computational Modeling
    Think Complexity : Complexity Science and Computational Modeling

    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.

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  • Innovation in Mixed Methods Research : A Practical Guide to Integrative Thinking with Complexity
    Innovation in Mixed Methods Research : A Practical Guide to Integrative Thinking with Complexity

    Explaining both why and how to use mixed methods for discovering solutions to complex research problems, this guide gives readers the tools to adapt approaches to suit their own research conditions.Written in a warm, encouraging tone and packed with helpful diagrams and visual organizers, it provides an easy-to-follow map to the mixed methods process, covering everything from ‘what is mixed methods research?’ to framing, integrating, and describing a complexity-sensitive mixed methods approach. Features include:Key questions to navigate the important concepts of each chapterPractice alerts to provide practical tips on working in the fieldChapter check-ins to assess development of key skillsFurther reading to expand and deepen knowledge of mixed methods practicesAn annotated glossary to get to grips with foundational terms and revise for examsSupported throughout by real-world examples and advice from the author and other mixed methods experts, this book helps readers succeed in their projects and think innovatively about the methods they use.

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  • Complexity in Organizations : A Research Overview
    Complexity in Organizations : A Research Overview

    Written with pace and clarity, this book is a comprehensive and compact overview and introduction to the research landscape of complexity in organizations.In addition to conveying a gripping history of how complexity has influenced organizational ideas, theories, and practices throughout the 20th century and into our present age, the book sheds light on how ground-breaking ideas in chaos and complexity research have emerged and challenged the very foundations of science into a changed vision of nature, society, and human organizations.As well as being an exciting investigation into complexity research in organizations, the book shows how, in the past, researchers who were immersed in the power politics of their day grappled with the theme of complexity in their quest to understand the dynamics of organization in nature and society.By welding fundamental theoretical themes and practical implications into the political and social contexts in which they emerged, this overview provides both depth and breadth to the history, as well as the future, of studies of complexity in organized activity.The book is a lucid and essential study of a topic that will be of interest to scholars, researchers, and students in the fields of business and management, especially those with an interest in the ways that complexity affects and transforms organizations.

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  • Unsimple Truths : Science, Complexity, and Policy
    Unsimple Truths : Science, Complexity, and Policy

    In "Unsimple Truths", Sandra D. Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences are providing about the world.She advocates, instead, for a new understanding that represents the rich, variegated, interdependent fabric of many levels and kinds of explanation that are integrated with one another to ground effective prediction and action.Mitchell draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend "integrative pluralism" - a theory of scientific practices that makes sense of how many natural and social sciences represent the multilevel, multicomponent, dynamic structures they study.Ultimately "Unsimple Truths" argues that the very idea of what should count as legitimate science itself should change.

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  • What is the complexity of semiconductor technology or microsystems technology?

    The complexity of semiconductor technology or microsystems technology is high due to the intricate processes involved in designing, manufacturing, and integrating tiny electronic components. These technologies require precise control at the nanoscale level, involving complex materials, intricate fabrication techniques, and sophisticated equipment. Additionally, the rapid pace of innovation and the need for continuous improvement in performance and miniaturization add to the complexity of these technologies. As a result, semiconductor and microsystems technology require significant expertise, resources, and investment to develop and produce advanced electronic devices.

  • What does the complexity class NP mean in computer science?

    In computer science, the complexity class NP (nondeterministic polynomial time) refers to a set of decision problems that can be verified in polynomial time. This means that given a potential solution to a problem, it can be efficiently checked to determine if it is correct. However, finding the solution itself may not be efficient, as it may require trying all possible solutions. NP problems are often associated with the concept of nondeterministic Turing machines, which can guess the correct solution and then verify it in polynomial time. The question of whether NP problems can be solved in polynomial time is one of the most important open problems in computer science, known as the P vs. NP problem.

  • Can complexity be objectively measured?

    Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity.

  • What is the complexity of Mergesort?

    The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets.

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  • Complexity Science : The Study of Emergence
    Complexity Science : The Study of Emergence

    Ecosystems, the human brain, ant colonies, and economic networks are all complex systems displaying collective behaviour, or emergence, beyond the sum of their parts.Complexity science is the systematic investigation of these emergent phenomena, and stretches across disciplines, from physics and mathematics, to biological and social sciences.This introductory textbook provides detailed coverage of this rapidly growing field, accommodating readers from a variety of backgrounds, and with varying levels of mathematical skill.Part I presents the underlying principles of complexity science, to ensure students have a solid understanding of the conceptual framework.The second part introduces the key mathematical tools central to complexity science, gradually developing the mathematical formalism, with more advanced material provided in boxes.A broad range of end of chapter problems and extended projects offer opportunities for homework assignments and student research projects, with solutions available to instructors online.Key terms are highlighted in bold and listed in a glossary for easy reference, while annotated reading lists offer the option for extended reading and research.

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  • Reflexive Mixed Methods Research in Comparative and International Education : Context, Complexity, and Transdisciplinarity
    Reflexive Mixed Methods Research in Comparative and International Education : Context, Complexity, and Transdisciplinarity

    Responding to both the increase of mixed methods research (MMR) and the lack of dialogue about its philosophical and methodological practices in comparative and international education (CIE) Reflexive Mixed Methods Research in Comparative and International Education: Context, Complexity, and Transdisciplinarity offers readers an overview of different philosophical approaches to mixed methods, as well as the practical processes to engage in MMR with reflexivity. Comparing reflexivity to a kaleidoscope, the author describes how it can be used throughout the research process to engage with different paradigms and methods, as well as to integrate findings and consider emerging interconnections.The book discusses various ways to reflexively adapt designs, methods, and analyses, and integrate findings in MMR to better contextualize knowledge in the field of CIE.It illustrates different approaches to MMR through various case summaries, and each chapter poses reflexive questions for researchers to consider during their research projects.It emphasizes a critical realist approach, particularly for how it involves epistemological pluralism while also offering contingent explanations to educational questions.Through a reflexive approach, the book offers possibilities for conducting better and more epistemically just MMR. This is fascinating reading for both new and more experienced researchers to expand the craft of researching with MM.Interdisciplinary and transdisciplinary teams, as well as sole researchers, will find concrete research practices in the book for employing reflexive MMR to understand complex educational issues.

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  • Research Methods for Complexity Theory in Applied Linguistics
    Research Methods for Complexity Theory in Applied Linguistics

    This book provides practical guidance on research methods and designs that can be applied to Complex Dynamic Systems Theory (CDST) research.It discusses the contribution of CDST to the field of applied linguistics, examines what this perspective entails for research and introduces practical methods and templates, both qualitative and quantitative, for how applied linguistics researchers can design and conduct research using the CDST framework.Introduced in the book are methods ranging from those in widespread use in social complexity, to more familiar methods in use throughout applied linguistics.All are inherently suited to studying both dynamic change in context and interconnectedness.This accessible introduction to CDST research will equip readers with the knowledge to ensure compatibility between empirical research designs and the theoretical tenets of complexity.It will be of value to researchers working in the areas of applied linguistics, language pedagogy and educational linguistics and to scholars and professionals with an interest in second/foreign language acquisition and complexity theory.

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  • Complexity and Values in Nurse Education : Dialogues on Professional Education
    Complexity and Values in Nurse Education : Dialogues on Professional Education

    This work explores the interplay of complexity and values in nurse education from a variety of vantages.Contributors, who come from a range of international and disciplinary backgrounds, critically engage important and problematic topics that are under-investigated elsewhere.Taking an innovative approach each chapter is followed by one or more responses and, on occasion, a reply to responses.This novel dialogic feature of the work tests, animates, and enriches the arguments being presented.Thought-provoking, challenging and occasionally rumbustious in tone, this volume has something to say to both nurse educators (who may find cherished practices questioned) and students.Given the breadth and nature of subjects covered, the book will also appeal to anyone concerned about and interested in nursing’s professional development/trajectory.

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  • How can one get rid of complexity?

    One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations.

  • What is the complexity of composing two functions?

    Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input.

  • What are the Landau symbols for the time complexity?

    The Landau symbols for time complexity are commonly used to describe the upper and lower bounds of an algorithm's running time. The most commonly used Landau symbols for time complexity are O (big O) for upper bound, Ω (big omega) for lower bound, and Θ (big theta) for both upper and lower bounds. These symbols are used to express the growth rate of an algorithm's running time in terms of the input size. For example, if an algorithm has a time complexity of O(n^2), it means that the running time of the algorithm grows no faster than n^2 as the input size increases.

  • What are the Big O notations for time complexity?

    The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms.

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