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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Rethinking Management : Radical Insights from the Complexity Sciences
What do business school graduates learn, and how helpful is it for managing in the everyday, messy reality of organisations?What does it mean to apply 'best practice', or to take up 'evidence-based management' and what kind of thinking does this imply? In Rethinking Management, Chris Mowles argues that many management courses still largely assume a linear and predictable world, when experience tells us that the opposite is the case.He questions some of the more orthodox conceptual assumptions that underpin much management education and instead, encourages leaders and managers to take their everyday experience of working with others seriously.People in organisations co-operate and compete to get things done, and constrain and enable each other in relationships of power. Because of this there are always unintended consequences of our actions - uncertainty is inherent in the everyday.Chris Mowles draws on the complexity sciences, the sciences of uncertainty rather than certainty, and the social sciences to explore more helpful ways to think and talk about our lived reality.He takes concrete examples from contemporary organisations, to argue that understanding the radical implications of uncertainty is central to the task of leading. Rethinking Management explores narrative alternatives to the ubiquitous grids and frameworks that are routinely taught in business schools, and encourages management professionals and educators to recognise the importance of judgement, improvisation and the everyday politics of organisational life.
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Critical Systems Thinking and the Management of Complexity
From the winner of the INCOSE Pioneer Award 2022The world has become increasingly networked and unpredictable.Decision makers at all levels are required to manage the consequences of complexity every day.They must deal with problems that arise unexpectedly, generate uncertainty, are characterised by interconnectivity, and spread across traditional boundaries.Simple solutions to complex problems are usually inadequate and risk exacerbating the original issues. Leaders of international bodies such as the UN, OECD, UNESCO and WHO — and of major business, public sector, charitable, and professional organizations — have all declared that systems thinking is an essential leadership skill for managing the complexity of the economic, social and environmental issues that confront decision makers.Systems thinking must be implemented more generally, and on a wider scale, to address these issues. An evaluation of different systems methodologies suggests that they concentrate on different aspects of complexity.To be in the best position to deal with complexity, decision makers must understand the strengths and weaknesses of the various approaches and learn how to employ them in combination.This is called critical systems thinking. Making use of over 25 case studies, the book offers an account of the development of systems thinking and of major efforts to apply the approach in real-world interventions.Further, it encourages the widespread use of critical systems practice as a means of ensuring responsible leadership in a complex world. The INCOSE Pioneer Award is presented to someone who, by their achievements in the engineering of systems, has contributed uniquely to major products or outcomes enhancing society or meeting its needs.The criteria may apply to a single outstanding outcome or a lifetime of significant achievements in effecting successful systems. Comments on a previous version of the book: Russ Ackoff: ‘the book is the best overview of the field I have seen’ JP van Gigch: ‘Jackson does a masterful job.The book is lucid ...well written and eminently readable’ Professional Manager (Journal of the Chartered Management Institute): ‘Provides an excellent guide and introduction to systems thinking for students of management’
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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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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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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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Domain-Driven Design : Tackling Complexity in the Heart of Software
Incorporate effective domain modeling into the software development process Software design thought leader and founder of Domain Language, Eric Evans, provides a systematic approach to domain-driven design, presenting an extensive set of design best practices, experience-based techniques, and fundamental principles that facilitate the development of software projects facing complex domains.Intertwining system design and development practice, this book incorporates numerous examples based on actual projects to illustrate the application of domain-driven design to real-world software modeling and development. Domain Model: Part I outlines the goals of domain-driven development, defines terms, and gives an overview of the implications of using the domain model to drive communication and designModel-Driven Design: Part II condenses a core of best practices in object-oriented domain modeling into a set of basic building blocks and focuses on the kinds of decisions that keep the model and implementation aligned with each other, each reinforcing the other’s effectivenessRefactoring: Part III delves into modeling principles that can guide choices along the way, and techniques that help direct the searchStrategic Design: Part IV explores a triad of principles that apply to the system as a whole: context, distillation, and large-scale structure Throughout the book, discussions are illustrated not with over-simplified, “” problems, but with realistic examples adapted from actual projects.With this book in hand, object-oriented developers, system analysts, and designers will have the guidance they need to organize and focus their work, create rich and useful domain models, and leverage those models into quality, long-lasting software implementations. “The book is a fun read. Eric has lots of interesting stories, and he has a way with words.I see this book as essential reading for software developers—it is a future classic.” —Ralph Johnson, author of Design Patterns
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Complexity : A Key Idea for Business and Society
This book interprets insights from the complexity sciences to explore seven types of complexity better to understand the predictable unpredictability of social life.Drawing on the natural and social sciences, it describes how complexity models are helpful but insufficient for our understanding of complex reality.Taking an interdisciplinary approach, the book develops a complex theory of action more consistent with our experience that our plans inevitably lead to unexpected outcomes, explains why we are both individuals and thoroughly social, and gives an account of why, no matter how clear our message, we may still be misunderstood.The book investigates what forms of knowledge are most helpful for thinking about complex experience, reflects on the way we exercise authority (leadership) and thinks through the ethical implications of trying to co-operate in a complex world.Taking complexity seriously poses a radical challenge to more orthodox theories of managing and leading, based as they are on assumptions of predictability, control and universality.The author argues that management is an improvisational practice which takes place in groups in a particular context at a particular time.Managers can influence but never control an uncontrollable world.To become more skilful in complex group dynamics involves taking into account multiple points of view and acknowledging not knowing, ambivalence and doubt. This book will be of interest to researchers, professionals, academics and students in the fields of business and management, especially those interested in how taking complexity seriously can influence the functioning of businesses and organizations and how they manage and lead.
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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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