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The Uncertainties of Knowledge extends Immanuel Wallerstein's decade-long work of elucidating the crisis of knowledge in current intellectual thought. He argues that the disciplinary divisions of academia have trapped us in a paradigm that assumes knowledge is a certainty and that it can help us explain the social world. This is wrong, he suggests. Instead, Wallerstein offers a new conception of the social sciences, one whose methodology allows for uncertainties. Author note: Immanuel Wallerstein is Director of the Fernand Braudel Center, Binghamton University, and Senior Research Scholar at Yale University.
Book Synopsis The Uncertainties of Knowledge by : Immanuel Maurice Wallerstein
Download or read book The Uncertainties of Knowledge written by Immanuel Maurice Wallerstein and published by . This book was released on 2004 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Uncertainties of Knowledge extends Immanuel Wallerstein's decade-long work of elucidating the crisis of knowledge in current intellectual thought. He argues that the disciplinary divisions of academia have trapped us in a paradigm that assumes knowledge is a certainty and that it can help us explain the social world. This is wrong, he suggests. Instead, Wallerstein offers a new conception of the social sciences, one whose methodology allows for uncertainties. Author note: Immanuel Wallerstein is Director of the Fernand Braudel Center, Binghamton University, and Senior Research Scholar at Yale University.
In The Uncertainties of Knowledge, Immanuel Wallerstein extends his work over the last decade of elucidating the crisis of knowledge in current intellectual thought. Arguing that the current disciplinary divisions of academia - divisions produced by a previous crisis of knowledge - has left us trapped in a paradigm that assumes knowledge is a certainty that can help us explain the social world, Wallerstein offers us a new way of imagining the social sciences, one which allows for uncertainties and for methods of studying our world and its historical place.
Book Synopsis The Uncertainties of Knowledge by : Immanuel Maurice Wallerstein
Download or read book The Uncertainties of Knowledge written by Immanuel Maurice Wallerstein and published by . This book was released on 2004 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: In The Uncertainties of Knowledge, Immanuel Wallerstein extends his work over the last decade of elucidating the crisis of knowledge in current intellectual thought. Arguing that the current disciplinary divisions of academia - divisions produced by a previous crisis of knowledge - has left us trapped in a paradigm that assumes knowledge is a certainty that can help us explain the social world, Wallerstein offers us a new way of imagining the social sciences, one which allows for uncertainties and for methods of studying our world and its historical place.
Book Synopsis The Uncertainties of Knowledge by : Immanuel Maurice Wallerstein
Download or read book The Uncertainties of Knowledge written by Immanuel Maurice Wallerstein and published by Temple University Press. This book was released on 2004 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Jonathan Fields knows the risks-and potential power-of uncertainty. He gave up a six-figure income as a lawyer to make $12 an hour as a personal trainer. Then, married with a 3-month old baby, he signed a lease to launch a yoga center in the heart of New York City. . . the day before 9/11. But he survived, and along the way he developed a fresh approach to transforming uncertainty, risk of loss, and exposure to judgment into catalysts for innovation, creation, and achievement. In business, art, and life, creating on a world-class level demands bold action and leaps of faith in the face of great uncertainty. But that uncertainty can lead to fear, anxiety, paralysis, and destruction. It can gut creativity and stifle innovation. It can keep you from taking the risks necessary to do great work and craft a deeply-rewarding life. And it can bring companies that rely on innovation grinding to a halt. That is, unless you know how to use it to your advantage. Fields draws on leading-edge technology, cognitive science, and ancient awareness-focusing techniques in a fresh, practical, nondogmatic way. His approach enables creativity and productivity on an entirely different level and can turn the once-tortuous journey into a more enjoyable quest.
Book Synopsis Uncertainty by : Jonathan Fields
Download or read book Uncertainty written by Jonathan Fields and published by Penguin. This book was released on 2012-11-06 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jonathan Fields knows the risks-and potential power-of uncertainty. He gave up a six-figure income as a lawyer to make $12 an hour as a personal trainer. Then, married with a 3-month old baby, he signed a lease to launch a yoga center in the heart of New York City. . . the day before 9/11. But he survived, and along the way he developed a fresh approach to transforming uncertainty, risk of loss, and exposure to judgment into catalysts for innovation, creation, and achievement. In business, art, and life, creating on a world-class level demands bold action and leaps of faith in the face of great uncertainty. But that uncertainty can lead to fear, anxiety, paralysis, and destruction. It can gut creativity and stifle innovation. It can keep you from taking the risks necessary to do great work and craft a deeply-rewarding life. And it can bring companies that rely on innovation grinding to a halt. That is, unless you know how to use it to your advantage. Fields draws on leading-edge technology, cognitive science, and ancient awareness-focusing techniques in a fresh, practical, nondogmatic way. His approach enables creativity and productivity on an entirely different level and can turn the once-tortuous journey into a more enjoyable quest.
Anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings by referring to the uncertainties in these areas of research. Uncertainty: How It Makes Science Advance shows that uncertainty is an inherent feature of science that makes it advance by motivating further research.
Book Synopsis Uncertainty by : Kostas Kampourakis
Download or read book Uncertainty written by Kostas Kampourakis and published by Oxford University Press, USA. This book was released on 2020 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings by referring to the uncertainties in these areas of research. Uncertainty: How It Makes Science Advance shows that uncertainty is an inherent feature of science that makes it advance by motivating further research.
Why is uncertainty so important to politics today? To explore the underlying reasons, issues and challenges, this book’s chapters address finance and banking, insurance, technology regulation and critical infrastructures, as well as climate change, infectious disease responses, natural disasters, migration, crime and security and spirituality and religion. The book argues that uncertainties must be understood as complex constructions of knowledge, materiality, experience, embodiment and practice. Examining in particular how uncertainties are experienced in contexts of marginalisation and precarity, this book shows how sustainability and development are not just technical issues, but depend deeply on political values and choices. What burgeoning uncertainties require lies less in escalating efforts at control, but more in a new – more collective, mutualistic and convivial – politics of responsibility and care. If hopes of much-needed progressive transformation are to be realised, then currently blinkered understandings of uncertainty need to be met with renewed democratic struggle. Written in an accessible style and illustrated by multiple case studies from across the world, this book will appeal to a wide cross-disciplinary audience in fields ranging from economics to law to science studies to sociology to anthropology and geography, as well as professionals working in risk management, disaster risk reduction, emergencies and wider public policy fields.
Book Synopsis The Politics of Uncertainty by : Ian Scoones
Download or read book The Politics of Uncertainty written by Ian Scoones and published by Routledge. This book was released on 2020-07-14 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why is uncertainty so important to politics today? To explore the underlying reasons, issues and challenges, this book’s chapters address finance and banking, insurance, technology regulation and critical infrastructures, as well as climate change, infectious disease responses, natural disasters, migration, crime and security and spirituality and religion. The book argues that uncertainties must be understood as complex constructions of knowledge, materiality, experience, embodiment and practice. Examining in particular how uncertainties are experienced in contexts of marginalisation and precarity, this book shows how sustainability and development are not just technical issues, but depend deeply on political values and choices. What burgeoning uncertainties require lies less in escalating efforts at control, but more in a new – more collective, mutualistic and convivial – politics of responsibility and care. If hopes of much-needed progressive transformation are to be realised, then currently blinkered understandings of uncertainty need to be met with renewed democratic struggle. Written in an accessible style and illustrated by multiple case studies from across the world, this book will appeal to a wide cross-disciplinary audience in fields ranging from economics to law to science studies to sociology to anthropology and geography, as well as professionals working in risk management, disaster risk reduction, emergencies and wider public policy fields.
Book Synopsis An Introduction to Error Analysis by : John Robert Taylor
Download or read book An Introduction to Error Analysis written by John Robert Taylor and published by Univ Science Books. This book was released on 1997-01-01 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problems after each chapter
This book is written for anyone who is interested in how a field of research evolves and the fundamental role of understanding uncertainties involved in different levels of analysis, ranging from macroscopic views to meso- and microscopic ones. We introduce a series of computational and visual analytic techniques, from research areas such as text mining, deep learning, information visualization and science mapping, such that readers can apply these tools to the study of a subject matter of their choice. In addition, we set the diverse set of methods in an integrative context, that draws upon insights from philosophical, sociological, and evolutionary theories of what drives the advances of science, such that the readers of the book can guide their own research with their enriched theoretical foundations. Scientific knowledge is complex. A subject matter is typically built on its own set of concepts, theories, methodologies and findings, discovered by generations of researchers and practitioners. Scientific knowledge, as known to the scientific community as a whole, experiences constant changes. Some changes are long-lasting, whereas others may be short lived. How can we keep abreast of the state of the art as science advances? How can we effectively and precisely convey the status of the current science to the general public as well as scientists across different disciplines? The study of scientific knowledge in general has been overwhelmingly focused on scientific knowledge per se. In contrast, the status of scientific knowledge at various levels of granularity has been largely overlooked. This book aims to highlight the role of uncertainties, in developing a better understanding of the status of scientific knowledge at a particular time, and how its status evolves over the course of the development of research. Furthermore, we demonstrate how the knowledge of the types of uncertainties associated with scientific claims serves as an integral and critical part of our domain expertise.
Book Synopsis Representing Scientific Knowledge by : Chaomei Chen
Download or read book Representing Scientific Knowledge written by Chaomei Chen and published by Springer. This book was released on 2017-11-25 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is written for anyone who is interested in how a field of research evolves and the fundamental role of understanding uncertainties involved in different levels of analysis, ranging from macroscopic views to meso- and microscopic ones. We introduce a series of computational and visual analytic techniques, from research areas such as text mining, deep learning, information visualization and science mapping, such that readers can apply these tools to the study of a subject matter of their choice. In addition, we set the diverse set of methods in an integrative context, that draws upon insights from philosophical, sociological, and evolutionary theories of what drives the advances of science, such that the readers of the book can guide their own research with their enriched theoretical foundations. Scientific knowledge is complex. A subject matter is typically built on its own set of concepts, theories, methodologies and findings, discovered by generations of researchers and practitioners. Scientific knowledge, as known to the scientific community as a whole, experiences constant changes. Some changes are long-lasting, whereas others may be short lived. How can we keep abreast of the state of the art as science advances? How can we effectively and precisely convey the status of the current science to the general public as well as scientists across different disciplines? The study of scientific knowledge in general has been overwhelmingly focused on scientific knowledge per se. In contrast, the status of scientific knowledge at various levels of granularity has been largely overlooked. This book aims to highlight the role of uncertainties, in developing a better understanding of the status of scientific knowledge at a particular time, and how its status evolves over the course of the development of research. Furthermore, we demonstrate how the knowledge of the types of uncertainties associated with scientific claims serves as an integral and critical part of our domain expertise.
This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.
Book Synopsis Uncertainty Quantification and Predictive Computational Science by : Ryan G. McClarren
Download or read book Uncertainty Quantification and Predictive Computational Science written by Ryan G. McClarren and published by Springer. This book was released on 2018-11-23 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.
Much economic advice is bogus quantification, warn two leading experts in this essential book, now with a preface on COVID-19. Invented numbers offer a false sense of security; we need instead robust narratives that give us the confidence to manage uncertainty. “An elegant and careful guide to thinking about personal and social economics, especially in a time of uncertainty. The timing is impeccable." — Christine Kenneally, New York Times Book Review Some uncertainties are resolvable. The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible. Before President Barack Obama made the fateful decision to send in the Navy Seals, his advisers offered him wildly divergent estimates of the odds that Osama bin Laden would be in the Abbottabad compound. In 2000, no one—not least Steve Jobs—knew what a smartphone was; how could anyone have predicted how many would be sold in 2020? And financial advisers who confidently provide the information required in the standard retirement planning package—what will interest rates, the cost of living, and your state of health be in 2050?—demonstrate only that their advice is worthless. The limits of certainty demonstrate the power of human judgment over artificial intelligence. In most critical decisions there can be no forecasts or probability distributions on which we might sensibly rely. Instead of inventing numbers to fill the gaps in our knowledge, we should adopt business, political, and personal strategies that will be robust to alternative futures and resilient to unpredictable events. Within the security of such a robust and resilient reference narrative, uncertainty can be embraced, because it is the source of creativity, excitement, and profit.
Book Synopsis Radical Uncertainty: Decision-Making Beyond the Numbers by : John Kay
Download or read book Radical Uncertainty: Decision-Making Beyond the Numbers written by John Kay and published by W. W. Norton & Company. This book was released on 2020-03-17 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: Much economic advice is bogus quantification, warn two leading experts in this essential book, now with a preface on COVID-19. Invented numbers offer a false sense of security; we need instead robust narratives that give us the confidence to manage uncertainty. “An elegant and careful guide to thinking about personal and social economics, especially in a time of uncertainty. The timing is impeccable." — Christine Kenneally, New York Times Book Review Some uncertainties are resolvable. The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible. Before President Barack Obama made the fateful decision to send in the Navy Seals, his advisers offered him wildly divergent estimates of the odds that Osama bin Laden would be in the Abbottabad compound. In 2000, no one—not least Steve Jobs—knew what a smartphone was; how could anyone have predicted how many would be sold in 2020? And financial advisers who confidently provide the information required in the standard retirement planning package—what will interest rates, the cost of living, and your state of health be in 2050?—demonstrate only that their advice is worthless. The limits of certainty demonstrate the power of human judgment over artificial intelligence. In most critical decisions there can be no forecasts or probability distributions on which we might sensibly rely. Instead of inventing numbers to fill the gaps in our knowledge, we should adopt business, political, and personal strategies that will be robust to alternative futures and resilient to unpredictable events. Within the security of such a robust and resilient reference narrative, uncertainty can be embraced, because it is the source of creativity, excitement, and profit.
