Products related to Complexity:
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Non-Linear Perspectives on Teacher Development : Complexity in Professional Learning and Practice
Despite the multifaceted complexity of teaching, dominant perspectives conceptualize teacher development in linear, dualistic, transactional, human-centric ways.The authors in this book offer non-linear alternatives by drawing on a continuum of complex perspectives, including CHAT, complexity theory, actor network theory, indigenous studies, rhizomatics, and posthuman/neomaterialisms.The chapters included here illuminate how different ways of thinking can help us better examine how teachers learn (relationally, with human, material, and discursive elements) and offer ways to understand the entangled nature of the relationship between that learning and what emerges in classroom instructional practice.They also present situated illustrations of what those entanglements or assemblages look like in the preservice, induction, and inservice phases, from early childhood to secondary settings, and across multiple continents.Authors provide evidence that research on teacher development should focus on process as much (if not more than) product and show that complexity perspectives can support forward-thinking, assets-based pedagogies.Methodologically, the chapters encourage conceptual creativity and expansion, and support an argument for blurring theory-method and normalising methodological hybridity.Ultimately, this book provides conceptual, theoretical, and methodological tools to understand current educational conditions in late capitalism and imagine otherwise.It was originally published as a special issue of the journal Professional Development in Education.
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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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Animal Social Complexity : Intelligence, Culture, and Individualized Societies
The editors of this volume argue that future research into complex animal societies and intelligence will change the perception of animals as gene machines, programmed to act in particular ways and perhaps elevate them to a status much closer to our own.At a time when humans are perceived more biologically than ever before, and animals as more cultural, are we about to witness the dawn of a truly unified social science, one with a distinctly cross-specific perspective?
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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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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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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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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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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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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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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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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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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.
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