Products related to Complexity:
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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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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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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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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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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.
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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.
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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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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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Simplified Complexity
Thanks to the growth of computational power and the development of new productiontechnologies, NURBS modeling has become the standard in many fields:Industrial Design, Architecture and, more recently, Engineering.Simplified Complexity is a method for learning NURBS modeling with Rhinoceros (R).Born as the synthesis of twenty years of professional experience and teaching,Simplified Complexity consists of a structured knowledge system allowing deepunderstanding of the software.With this method the user can take advantage of Rhinoceros (R) full modeling potential.The idea behind Simplified Complexity is that even if the software has a clear andintuitive interface, NURBS geometry remains quite complex.In order to become aprofessional user, it is necessary to start from basic geometry knowledge: this willallow to foresee and avoid complexity or, if this is not possible, at least reduce it andoptimize it.
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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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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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The Theory and Practice of Relational Coaching : Complexity, Paradox and Integration
The 'relational turn' is a movement affecting a range of disciplines including neuroscience, psychoanalysis, psychotherapy, organisational consulting and, more recently, coaching.Its primary focus is on the centrality of human relating in determining how individuals develop, make meaning and function individually and collectively.In The Theory and Practice of Relational Coaching: Complexity, Paradox and Integration, Simon Cavicchia and Maria Gilbert expand existing coaching theory and practice to focus on the implications of the relational turn for how coaches and clients think about the nature of identity, the self, change, learning, and individual and organisational development.Drawing on perspectives as varied as relational neuroscience, the relational foundations of personality development, psychoanalysis, psychotherapy, shame, vulnerability, complexity and systems ideas, the authors shed light on many of the paradoxes and challenges facing coaches and their clients in today’s fast-paced, volatile and uncertain organisational environments.These include holding tensions such as the uniqueness of individual needs with the requirements of organisational contexts, managing multiple stakeholder expectations and networks and balancing linear approaches to change with adjusting to emerging and unpredictable events. Given the ever-increasing volatility, complexity and uncertainty that coaches and their clients face, The Theory and Practice of Relational Coaching guides the reader through a series of illuminating perspectives, examples and practical suggestions.These will enable coaches to integrate a more relational orientation in their work and extend their range and that of their clients for responding creatively to the challenges of modern organisational life.The book will appeal to coaches and coaching psychologists in practice and training, as well as counsellors and psychotherapists retraining as coaches.
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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.
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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.
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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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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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