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• 1. Preface--
• 2. Views on Uncertainty--
• 3. Introduction--
• 4. Part one: the definition of an order of possibility and the removal of uncertainty--
• 5. 1.1 Between the Austrian Marginalists and Weber: ideal-typical &#34--purity&#34-- and formal models--
• 6. 1.2 Innovative decisions, logic of expectation and objective possibility in Weber--
• 7. 1.3 Order and &#34--Pure&#34-- Theory of Law--
• 8. 1.4 Dynamic cycle, monetary calculation and uncertainty--
• 9. 1.5 Money control, assurance and politics in the German-speaking world--
• 10. 1.6 Uncertainty and the monetary stability in Keynes&#39-- works of the 1920&#39--s--
• 11. 1.7 The foundation of rational expectation in Keynes&#39-- &#34--Treatise on Probability&#34-- and the Cambridge logicists--
• 12. 1.8 Against &#39--insecuritas&#39--: the phenomenological neutralization of uncertainty--
• 13. 1.9 Phenomenology and formalization--
• 14. 1.10 Hilbert and Husserl: forms, intuition and certainty--
• 15. 1.11 Between the early Heidegger and the neo-Kantianism of the Marburg School: the forms as ordering relations--
• 16. Notes--
• 17. Part two: towards uncertainty--
• 18. 2.1 Keynes and the thirties: uncertainty, gambling and money--
• 19. 2.2 The thirties: effective demand and the social State--
• 20. 2.3 Phenomenology as a genetic-recompositive research programme--
• 21. 2.4 Original intuition, life-world and apodictic certainty--
• 22. 2.5 Heidegger: revealing the instability and contingency of the ontic dimension as a necessary premise to a theology of absence--
• 23. Notes--
• 24. Uncertainty: alternative conceptual outlooks and socio-political models--
• 25. Introduction--
• 26. Part one: uncertainty and game against &#39--Nature&#39----
• 27. 1.1 Conceptual premise: the ramified tree of events--
• 28. 1.2 General Equilibrium Theory--
• 29. 1.3 &#39--Laissez-faire&#39-- Monetarism and Rational Expectations Hypothesis--
• 30. 1.4 Keynesian &#39--Eclecticism&#39-- and economic policy--
• 31. 1.5 Disequilibrium, private choice and system&#39--s rationality--
• 32. Part two: uncertainty and interactive game--
• 33. 2.1 Conceptual premise: the indeterministic structure of events--
• 34. 2.2 Groups, negotiated allocation of resources, conflicts and structural dynamics--
• 35. Notes.
• (source: Nielsen Book Data)

Scientific knowledge is the most solid and robust kind of knowledge that humans have because of the self-correcting character inherent in its own processes. Nevertheless, anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings, making claims that are unsupported by empirical evidence. A common aspect of these claims is the reference to the uncertainties in these areas of research, which leads to the conclusion that science is uncertain about evolution, climate change, and vaccination, among others. The truth of the matter is that while the broad picture is clear, there exist-and will always exist-uncertainties about the details of the respective phenomena. In this book Kampourakis and McCain show that uncertainty is an inherent feature of science that does not devalue it. In contrast, uncertainty actually makes science advance because it motivates further research. The first book of its kind, Uncertainty draws on philosophy of science to explain what uncertainty in science is and how it makes science advance. It contrasts evolution, climate change, and vaccination, where the uncertainties are exaggerated, to genetic testing and forensic science where the uncertainties are usually overlooked. Kampourakis and McCain discuss the scientific, psychological, and philosophical aspects of uncertainty in order to explain what it is really about, what kind of problems it actually poses, and why it ultimately makes science advance. Contrary to the public representations of scientific findings and conclusions that produce an intuitive but distorted view of science as certain, we need to understand and learn to live with uncertainty in science.
(source: Nielsen Book Data)

• Preface--
• 1. Introduction--
• 2. Recent milestones--
• 3. An overview of quantitative policy analysis--
• 4. The nature and sources of uncertainty--
• 5. Probability distributions and statistical estimation--
• 6. Human judgement about and with uncertainty--
• 7. Performing probability assessment--
• 8. The propagation and analysis of uncertainty--
• 9. The graphic communication of uncertainty--
• 10. Analytica: a software tool for uncertainty analysis--
• 11. Large and complex models--
• 12. The value of knowing how little you know-- Index.
• (source: Nielsen Book Data)

The authors explain the ways in which uncertainty is an important factor in the problems of risk and policy analysis. This book outlines the source and nature of uncertainty, discusses techniques for obtaining and using expert judgment, and reviews a variety of simple and advanced methods for analyzing uncertainty.
(source: Nielsen Book Data)

No abstract prepared.

• Preface--
• 1. Introduction--
• 2. Recent milestones--
• 3. An overview of quantitative policy analysis--
• 4. The nature and sources of uncertainty--
• 5. Probability distributions and statistical estimation--
• 6. Human judgement about and with uncertainty--
• 7. Performing probability assessment--
• 8. The propagation and analysis of uncertainty--
• 9. The graphic communication of uncertainty--
• 10. Analytica: a software tool for uncertainty analysis--
• 11. Large and complex models--
• 12. The value of knowing how little you know-- Index.
• (source: Nielsen Book Data)

The authors explain the ways in which uncertainty is an important factor in the problems of risk and policy analysis. This book outlines the source and nature of uncertainty, discusses techniques for obtaining and using expert judgment, and reviews a variety of simple and advanced methods for analyzing uncertainty.
(source: Nielsen Book Data)

Uncertainty influences the individual as well as society. Because of this it is important to enhance our understanding of uncertainty. Firstly, is there a unique definition of uncertainty in the natural and technological worlds? Secondly, how can we model uncertainty? Is it purely a state of mind, the lack of complete knowledge, or is it a phenomenon in its own right? What heuristic and mathematical models are available? What types of questions about uncertainty can be formulated? What questions can be realistically answered? Do scholars in diverse fields agree on these issues? These and other questions are addressed by scholars from mechanical, civil, electrical, material, aerospace and ocean engineering, from applied mathematics, economics, industrial engineering and operations research, control theory, geodesy, systems science, and philosophy.
(source: Nielsen Book Data)

• Preface--
• 1. Introduction--
• 2. Recent milestones--
• 3. An overview of quantitative policy analysis--
• 4. The nature and sources of uncertainty--
• 5. Probability distributions and statistical estimation--
• 6. Human judgement about and with uncertainty--
• 7. Performing probability assessment--
• 8. The propagation and analysis of uncertainty--
• 9. The graphic communication of uncertainty--
• 10. Analytica: a software tool for uncertainty analysis--
• 11. Large and complex models--
• 12. The value of knowing how little you know-- Index.
• (source: Nielsen Book Data)

The authors explain the ways in which uncertainty is an important factor in the problems of risk and policy analysis. This book outlines the source and nature of uncertainty, discusses techniques for obtaining and using expert judgment, and reviews a variety of simple and advanced methods for analyzing uncertainty.
(source: Nielsen Book Data)

Danish reporter Martin Molberg is driven by demons. His girlfriend's murder remains unsolved. Suspicion clings to him like a cold sweat. His mental state is crumbling under a blitz of Zantac and Demerol. Yet perhaps most disturbing is the grainy photograph of his sweet-tempered Monique and a shadowy stranger, caught in a sordid act of rough sex. This is not the woman he loved. Or is it? Fueled by an obsessive search for truth that takes him from Copenhagen to Los Angeles, Molberg unexpectedly uncovers something far greater and far more sinister: digital technology with ramifications so chilling, it will change the face of the future . . . and the ways we receive, interpret, and believe information.
(source: Nielsen Book Data)

• Truth, Argument, Realism
• Logic
• Induction and Intellection
• What Probability Is
• What Probability Is Not
• Chance and Randomness
• Causality
• Probability Models
• Statistical and Physical Models
• Modelling Goals, Strategies, and Mistakes.

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields ℓ́ℓ probability, physics, biology, the ℓ́ℓsoftℓ́ℓ sciences, computer science ℓ́ℓ because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling. Presents a complete argument showing why probability should be treated as a part of logic Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling Proposes that p-values should die, and along with them, hypothesis testing William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.

• Why uncertainty matters
• What uncertainty does to us
• The myth of the fearless creator
• Find your certainty anchors
• Build your hive
• Socializing creation
• Train your brain
• See the forest
• Own the storyline
• Bring it home.

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.Properly understood and harnessed, fear and uncertainty can become fuel for creative genius rather than sources of pain, anxiety, and suffering. 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, non-dogmatic way. His approach enables creativity and productivity on an entirely different level and can turn the once-tortuous journey into a more enjoyable quest. Fields will reveal how to: Make changes to your workflow that unlock buried creative potential. Build "creation hives" -- supportive groups that can supercharge and humanize the process. Tap social technology and user co-creation to add clarity, certainty, and sanity, even if you're an artist or solo-creator. Develop a set of personal practices and mindset shifts that let you not just tolerate, but invite and even amplify, uncertainty as a catalyst for genius. Drawing on extensive case studies and research, Fields shares a set of detailed personal practices and environmental changes that can not only humanize the creative process, but also allow individuals and teams to stay more open to opportunity and play a bigger creative game.
(source: Nielsen Book Data)

David Cassidy's portrait of this brilliant, ambitious and controversial scientist is the definitive Heisenberg biography. "An absorbing new biography of Werner Heisenberg ..." The Sunday Telegraph "Uncertainty is an exquisite book." Nature "...well balanced and exciting reading. Uncertainty is an excellent work which combines meticulous scholarship with a presentation freed of intellectual jargon and unnecessary scientific details. I see its merits first of all in its comprehensiveness and accuracy, and next in its success in recreating the personal drama of one of the greatest and most influential scientists of this century." Times HigherEducation Supplement.
(source: Nielsen Book Data)

The US nuclear waste disposal program is evaluating the Yucca Mountain (YM) site for possible disposal of nuclear waste. Radioactive decay of the waste, particularly spent fuel, generates sufficient heat to significantly raise repository temperatures. Environmental conditions in the repository system evolve in response to this heat. The amount of temperature increase, and thus environmental changes, depends on repository design and operations. Because the evolving environment cannot be directly measured until after waste is emplaced, licensing decisions must be based upon model and analytical projections of the environmental conditions. These analyses have inherent uncertainties. There is concern that elevated temperatures increase uncertainty, because most chemical reaction rates increase with temperature and boiling introduces additional complexity of vapor phase reactions and transport. This concern was expressed by the NWTRB, particularly for above boiling temperatures. They state that ''the cooler the repository, the lower the uncertainty about heat-driven water migration and the better the performance of waste package materials. Above this temperature, technical uncertainties tend to be significantly higher than those associated with below-boiling conditions.'' (Cohon 1999). However, not all uncertainties are reduced by lower temperatures, indeed some may even be increased. This paper addresses impacts of temperatures on uncertainties.