Products related to Complexity:
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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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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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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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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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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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Learning Functional Programming : Managing Code Complexity by Thinking Functionally
Learn how to think and write code like a functional programmer.With this practical guide, software developers familiar with object-oriented programming will dive into the core concepts of functional programming and learn how to use both functional and OOP features together on large or complex software projects. Author Jack Widman uses samples from Java, Python, C#, Scala, and JavaScript to help you gain a new perspective and a set of tools for managing the complexity in your problem domain.You'll be able to write code that's simpler, reusable, easier to test and modify, and more consistently correct.This book also shows you how to use patterns from category theory to help bridge the gap between OOP and functional programming. Learn functional programming fundamentals and explore the way functional programmers approach problemsUnderstand how FP differs from object-oriented and imperative programmingUse a set of practical, applicable design patterns that model reality in a functional wayLearn how to incorporate FP and OOP features into software projectsApply functional design patterns appropriately and use them to write correct, robust, and easily modifiable code
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Recentering Learning : Complexity, Resilience, and Adaptability in Higher Education
Is a renaissance of teaching and learning in higher education possible?One may already be underway. The COVID-19 pandemic fundamentally changed how colleges and universities manage teaching and learning.Recentering Learning unpacks the wide-reaching implications of disruptions such as the pandemic on higher education. Editors Maggie Debelius, Joshua Kim, and Edward Maloney assembled a diverse group of scholars and practitioners to assess the impacts of the pandemic, as well as to anticipate the effects of climate change, social unrest, artificial intelligence, financial challenges, changing demographics, and other forms of disruption, on teaching and learning.These contributors are leaders at their institutions and draw on both the Scholarship of Teaching and Learning (SoTL) as well as their lived experiences to draw important lessons for the wider postsecondary ecosystem.The collection features faculty, staff, and student voices from a range of public and private institutions of varying sizes and serving different populations. Covering timely topics such as institutional resiliency, how to create transformational change, digital education for access and equity, and the shifting institutional data landscape, these essays serve as a compelling guide for how colleges and universities can navigate inevitable changes to teaching and learning.Faculty and staff at centers for teaching excellence or centers for innovation, university leaders, graduate students in learning design programs, and anyone interested in the evolution of teaching and learning in the twenty-first century will benefit from this prescient volume. Contributors: Bryan Alexander, Drew Allen, Isis Artze-Vega, Betsy Barre, Randy Bass, MJ Bishop, Derek Bruff, Molly Chehak, Nancy Chick, Cynthia A.Cogswell, Jenae Cohn, Tazin Daniels, Maggie Debelius, David Ebenbach, Megan Eberhardt-Alstot, Kristen Eshleman, Peter Felten, Lorna Gonzalez, Michael Goudzwaard, Sophia Grabiec, Sean Hobson, Kashema Hutchinson, Amanda Irvin, Jonathan Iuzzini, Amy Johnson, Briana Johnson, Matthew Kaplan, Whitney Kilgore, Joshua Kim, Sujung Kim, Suzanna Klaf, Martin Kurzweil, Natalie Landman, Jill Leafstedt, Katie Linder, Sherry Linkon, Edward Maloney, Susannah McGowan, Isabel McHenry, Rolin Moe, Lillian Nagengast, Nancy O'Neill, Adashima Oyo, Matthew Rascoff, Libbie Rifkin, Katina Rogers, Catherine Ross, Annie Sadler, Monique L.Snowden, Elliott Visconsi, Mary Wright
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Complexity and Leadership
Leading organisations in our contemporary world means grappling with unpredictability, painful pressures and continual conflict, all in the context of an acceleration in the pace of change.We expect the impossible from heroic leaders and they rarely live up to expectations.With countless recommendations, self-help books and new concepts, scholars and management consultants often simplify and dream unrealistically.This book challenges the more orthodox discourse on leadership and presents a way of thinking about leadership that pays closer attention to experience. The contributors in this book, all senior managers or facilitators of leadership development, resist easy solutions, new typologies or unrealistic prescriptions.Writing about their experiences in Denmark, the UK, Israel, Ethiopia, South Africa and beyond, they are less concerned with traits that people can possess and learn, or magical promises of recipes for success, and more with the socio-political process of the interaction between people from which leadership emerges as a theme.We focus on understanding leadership as a practice within which communication, research, imagination and ethical judgements are continuously improvised.So rather than idealising leadership, or reducing it to soothing tools and techniques, we suggest how leaders might become more politically, emotionally and socially savvy.This book is written for academics and practitioners with an interest in the everyday challenges of both individual and group practices of formal and informal leaders in different types of organisations, and is an ideal resource for executives and students on leadership development programmes.We hope this volume will help readers to expand the wisdom found in their own experience and discover for themselves and for others, a greater sense of freedom.
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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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