Products related to Complexity:
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Human-Wildlife Conflict : Complexity in the Marine Environment
Human-wildlife conflict (HWC) has classically been defined as a situation where wildlife impacts humans negatively (physically, economically, or psychologically), and where humans likewise negatively impact wildlife.However, there is growing consensus that the conflict between people about wildlife is as important as the conflict between people and wildlife.HWC not only affects the conservation of one species in a particular geographic area, but also impacts the willingness of an individual, a community, and wider society to support conservation programs in general.This book explores the complexity inherent in these situations, covering the theory, principles, and practical applications of HWC work, making it accessible and usable for conservation practitioners, as well as of interest to researchers more concerned with a theoretical approach to the subject.Through a series of case studies, the book's authors and editors tackle a wide variety of subjects relating to conflict, from the challenges of wicked problems and common pool resources, to the roles that storytelling and religion can play in conflict.Throughout the book, the authors work with a Conservation Conflict Transformation (CCT) approach, adapted from the peacebuilding field to address the reality of conservation today.The authors utilise one of CCT's key analytic components, the Levels of Conflict model, as a tool to provide insight into their case studies.Although the examples discussed are from the world of marine conservation, the lessons they provide are applicable to a wide variety of global conservation issues, including those in the terrestrial realm.Human-Wildlife Conflict will be essential reading for graduate students and established researchers in the field of marine conservation biology.It will also be a valuable reference for a global audience of conservation practitioners, wildlife managers, and other conservation professionals.
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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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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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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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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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Elements of Deterrence : Strategy, Technology, and Complexity in Global Politics
Global politics in the twenty-first century is complicated by dense economic interdependence, rapid technological innovation, and fierce security competition.How should governments formulate grand strategy in this complex environment?Many strategists look to deterrence as the answer, but how much can we expect of deterrence?Classical deterrence theory developed in response to the nuclear threats of the Cold War, but strategists since have applied it to a variety of threats in the land, sea, air, space, and cyber domains.If war is the continuation of politics by other means, then the diversity of technologies in modern war suggests a diversity of political effects.Some military forces or postures are most useful for "winning" various kinds of wars.Others are effective for "warning" adversaries of consequences or demonstrating resolve.Still others may accomplish these goals at lower political cost, or with greater strategic stability.Deterrence is not a simple strategy, therefore, but a complex relationship between many ends and many means.This book presents findings from a decade-long research program on "cross-domain deterrence." Through a series of theoretical and empirical studies, we explore fundamental trade-offs that have always been implicit in practice but have yet to be synthesized into a general theory of deterrence.Gartzke and Lindsay integrate newly revised and updated versions of published work alongside new work into a holistic framework for understanding how deterrence works--or fails to work--in multiple domains.Their findings show that in deterrence, all good things do not go together.
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Living with Complexity
Why we don't really want simplicity, and how we can learn to live with complexity. If only today's technology were simpler! It's the universal lament, but it's wrong. In this provocative and informative book, Don Norman writes that the complexity of our technology must mirror the complexity and richness of our lives.It's not complexity that's the problem, it's bad design.Bad design complicates things unnecessarily and confuses us.Good design can tame complexity. Norman gives us a crash course in the virtues of complexity.Designers have to produce things that tame complexity.But we too have to do our part: we have to take the time to learn the structure and practice the skills.This is how we mastered reading and writing, driving a car, and playing sports, and this is how we can master our complex tools.Complexity is good. Simplicity is misleading. The good life is complex, rich, and rewarding—but only if it is understandable, sensible, and meaningful.
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Computability and Complexity
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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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