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  • Navigating the Complexity Across the Peace–Sustainability–Climate Security Nexus
    Navigating the Complexity Across the Peace–Sustainability–Climate Security Nexus

    Promoting peace and sustainability in human development while accounting for the risks associated with the impact of climate change on society has become more imperative than ever when addressing humanity's challenges of the twenty-first century.There is enough evidence that peace, sustainability, and climate security are entangled with multiple complex interactions and cannot be dealt with in isolation and independently from the environment and the numerous systems with which they interact.Yet, the intersection of peace, sustainability, and climate security or their opposites (i.e., conflict, unsustainability, and climate vulnerability) is rarely articulated with a systemic mindset.A multi-solving nexus approach is more appropriate to capture the complexity and uncertainty of how the three sectors of peace, sustainability, and climate security play a role in community development, the nature of their causal chains, and the feedback on how community development affects the three sectors.Navigating the Complexity Across the Peace–Sustainability–Climate Security Nexus explores the value proposition of using a systems approach, methodology, and tools to comprehend and model that dynamic. Features of the book: Explores the interaction between the different components of peace and the relationship between peace, sustainability, and climate security using semi-qualitative and quantitative tools; Explains how climate adaptation and mitigation are related to peace or conflict; Presents generic system dynamics modeling that can be used in different contexts.

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  • Leadership Development in Practice : A Complexity Approach
    Leadership Development in Practice : A Complexity Approach

    In an unpredictable world, how do we go about supporting leaders to develop more democratic and inclusive ways of working and living?The second edition of Leadership Development in Practice: A Complexity Approach draws on autoethnographic accounts of experience from practitioners across three continents to explore the leadership development approaches that best support managers to work with uncertainty by taking their experience seriously.It offers an alternative perspective on leadership and organisation for business schools, consultancies, and corporate training functions to adopt in their development of leaders. Additions to this second edition include as follows:A new chapter on creating large group dialogueA more explicit emphasis on what it means to take gender, diversity, and social justice seriouslyA review of the burgeoning interest in complexity perspectives on leadership and leadership development since publication of the first editionThis book is essential reading for leadership and organisational development professionals, researchers, and students.It will also be of interest to managers looking for an approach to leadership development that works with how things are rather than with idealisations of how things ought to be.

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  • Art and Sustainability : Connecting Patterns for a Culture of Complexity
    Art and Sustainability : Connecting Patterns for a Culture of Complexity

    What is the cultural dimension of sustainability? This book offers a thought-provoking answer, with a theoretical synthesis on »cultures of sustainability«.Describing how modernity degenerated into a culture of unsustainability, to which the arts are contributing, Sacha Kagan engages us in a fundamental rethinking of our ways of knowing and seeing the world.We must learn not to be afraid of complexity, and to re-awaken a sensibility to patterns that connect.With an overview of ecological art over the past 40 years, and a discussion of art and social change, the book assesses the potential role of art in a much needed transformation process.

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  • 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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  • 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.

  • 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.

  • 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.

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  • Creativity in Military Complexity : Design, Disruptors and Defence Forces
    Creativity in Military Complexity : Design, Disruptors and Defence Forces

    This work offers a groundbreaking exploration of the urgent need for creativity and innovation in contemporary military thought. In an era characterised by the ceaseless flux of global dynamics, traditional paradigms of warfare have become increasingly obsolete.The pursuit of victory no longer lies in the fixation upon past conflicts but rather in the discerning assessment of and adaptation to the challenges that will shape the conflicts of tomorrow.This innovative work dissects the barriers that have thus far hindered the realisation of this potential.Furthermore, it challenges the status quo and advances a series of recommendations poised to steer international militaries towards success in the theatre of contemporary conflicts.Drawing from their extensive involvement with defence forces worldwide, the authors introduce concepts such as military design thinking as catalytic instruments of change.Through introspective reflections and real-world case studies, they present implications for mitigating cognitive biases, heralding a transformative epoch in military operations.It is this very transformation that furnishes militaries with the capacity to surge ahead of their adversaries, a capability proving to be indispensable in modern warfare. Offering a well-illuminated path for military entities to adapt and flourish within an ever-evolving global landscape, this book will be of much interest to students of military studies, security studies, and international relations, as well as military professionals and leaders in the field. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

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  • Developmental Evaluation : Applying Complexity Concepts to Enhance Innovation and Use
    Developmental Evaluation : Applying Complexity Concepts to Enhance Innovation and Use

    Developmental evaluation (DE) offers a powerful approach to monitoring and supporting social innovations by working in partnership with program decision makers.In this book, eminent authority Michael Quinn Patton shows how to conduct evaluations within a DE framework.Patton draws on insights about complex dynamic systems, uncertainty, nonlinearity, and emergence.He illustrates how DE can be used for a range of purposes: ongoing program development, adapting effective principles of practice to local contexts, generating innovations and taking them to scale, and facilitating rapid response in crisis situations.Students and practicing evaluators will appreciate the book's extensive case examples and stories, cartoons, clear writing style, "closer look" sidebars, and summary tables.Provided is essential guidance for making evaluations useful, practical, and credible in support of social change. See also Developmental Evaluation Exemplars, edited by Michael Quinn Patton, Kate McKegg, and Nan Wehipeihana, which presents 12 in-depth case studies.

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  • Simply Complexity : A Clear Guide to Complexity Theory
    Simply Complexity : A Clear Guide to Complexity Theory

    What do traffic jams, stock market crashes, and wars have in common?They are all explained using complexity, an unsolved puzzle that many researchers believe is the key to predicting – and ultimately solving—everything from terrorist attacks and pandemic viruses right down to rush hour traffic congestion. Complexity is considered by many to be the single most important scientific development since general relativity and it promises to make sense of no less than the very heart of the Universe.Using it, scientists can find order emerging from seemingly random interactions of all kinds, from something as simple as flipping coins through to more challenging problems such as the patterns in modern jazz, the growth of cancer tumours, and predicting shopping habits.

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  • Ecological Complexity
    Ecological Complexity

    Complexity has received substantial attention from scientists and philosophers alike.There are numerous, often conflicting, accounts of how complexity should be defined and how it should be measured.Much less attention has been paid to the epistemic implications of complexity, especially in Ecology.How does the complex nature of ecological systems affect ecologists' ability to study them?This Element argues that ecological systems are complex in a rather special way: they are causally heterogeneous.Not only are they made up of many interacting parts, but their behaviour is variable across space or time.Causal heterogeneity is responsible for many of the epistemic difficulties that ecologists face, especially when making generalisations and predictions.Luckily, ecologists have the tools to overcome these difficulties, though these tools have historically been considered suspect by philosophers of science.The author presents an updated philosophical account with an optimistic outlook of the methods and status of ecological research.

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  • 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.

  • How do you determine the complexity of a function?

    The complexity of a function can be determined by analyzing its time and space requirements. This can be done by examining the number of operations the function performs and the amount of memory it uses. Additionally, the complexity can be influenced by the size of the input data and the efficiency of the algorithm used in the function. By considering these factors, one can determine the complexity of a function, which is often expressed using Big O notation.

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