This book is an extensive survey and critical examination of the literature on the use of expert opinion in scientific inquiry and policy making. The elicitation, representation, and use of expert opinion is increasingly important for two reasons: advancing technology leads to more and more complex decision problems, and technologists are turning in greater numbers to "expert systems" and other similar artifacts of artificial intelligence. Cooke here considers how expert opinion is being used today, how an expert's uncertainty is or should be represented, how people do or should reason with uncertainty, how the quality and usefulness of expert opinion can be assessed, and how the views of several experts might be combined. He argues for the importance of developing practical models with a transparent mathematic foundation for the use of expert opinion in science, and presents three tested models, termed "classical," "Bayesian," and "psychological scaling." Detailed case studies illustrate how they can be applied to a diversity of real problems in engineering and planning.

Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide range of research findings in order to elicit expert judgements accurately and reliably. Uncertain Judgements introduces the area, before guiding the reader through the study of appropriate elicitation methods, illustrated by a variety of multi-disciplinary examples. This is achieved by: Presenting a methodological framework for the elicitation of expert knowledge incorporating findings from both statistical and psychological research. Detailing techniques for the elicitation of a wide range of standard distributions, appropriate to the most common types of quantities. Providing a comprehensive review of the available literature and pointing to the best practice methods and future research needs. Using examples from many disciplines, including statistics, psychology, engineering and health sciences. Including an extensive glossary of statistical and psychological terms. An ideal source and guide for statisticians and psychologists with interests in expert judgement or practical applications of Bayesian analysis, Uncertain Judgements will also benefit decision-makers, risk analysts, engineers and researchers in the medical and social sciences.

Experts, despite their importance and value, can be double-edged swords. They can make valuable contributions from their deep base of knowledge, but those contributions may also contain their own biases and pet theories. Therefore, selecting experts, eliciting their opinions, and aggregating their opinions must be performed and handled carefully, with full recognition of the uncertainties inherent in those opinions. Elicitation of Expert Opinions for Uncertainty and Risks illuminates those uncertainties and builds a foundation of philosophy, background, methods, and guidelines that helps its readers effectively execute the elicitation process. Based on the first-hand experiences of the author, the book is filled with illustrations, examples, case studies, and applications that demonstrate not only the methods and successes of expert opinion elicitation, but also its pitfalls and failures. Studies show that in the future, analysts, engineers, and scientists will need to solve ever more complex problems and reach decisions with limited resources. This will lead to an increased reliance on the proper treatment of uncertainty and on the use of expert opinions. Elicitation of Expert Opinions for Uncertainty and Risks will help prepare you to better understand knowledge and ignorance, to successfully elicit expert opinions, to select appropriate expressions of those opinions, and to use various methods to model and aggregate opinions.

3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented.

A risk analysis textbook which is intended as a basic text for students as well as a reference for practitioners and researchers. It provides a basis for policy analysis and draws upon a variety of case studies.

This book explains the notational system NUSAP (Numeral, Unit, Spread, Assessment, Pedigree) and applies it to several examples from the environmental sciences. The authors are now making further extensions of NUSAP, including an algorithm for the propagation of quality-grades through models used in risk and safety studies. They are also developing the concept of `Post-normal Science', in which quality assurance of information requires the participation of `extended peer-communities' lying outside the traditional expertise.

Uncertainty in Policy Making explores how uncertainty is interpreted and used by policy makers, experts and politicians. It argues that conventional notions of rational, evidence-based policy making - hailed by governments and organisations across the world as the only way to make good policy - is an impossible aim in highly complex and uncertain environments; the blind pursuit of such a 'rational' goal is in fact irrational in a world of competing values and interests. The book centres around two high-profile and important case studies: the Iraq war and climate change policy in the US, UK and Australia. Based on three years' research, including interviews with experts such as Hans Blix, Paul Pillar, and Brian Jones, these two case studies show that the treatment of uncertainty issues in specialist advice is largely determined by how well the advice fits with or contradicts the policy goals and orientation of the policy elite. Instead of allowing the debates to be side-tracked by arguments over whose science or expert advice is 'more right', we must accept that uncertainty in complex issues is unavoidable and recognise the values and interests that lie at the heart of the issues. The book offers a 'hedging' approach which will enable policy makers to manage rather than eliminate uncertainty.

Expert judgment is invaluable for assessing products, systems, and situations for which measurements or test results are sparse or nonexistent. Eliciting and Analyzing Expert Judgment: A Practical Guide takes the reader step by step through the techniques of eliciting and analyzing expert judgment, with special attention given to helping the reader develop elicitation methods and tools adaptable to a variety of unique situations and work areas. The analysis procedures presented in the book may require a basic understanding of statistics and probabilities, but the authors have provided detailed explanations of the techniques used and have taken special care to define all statistical jargon. Originally published in 1991, this book is designed so that those familiar with the use of expert judgment can quickly find the material appropriate for their advanced background.

Whatever your ambitions, ideas and challenges, this book will revolutionize the way you live, think and work today, and tomorrow. Pirates didn't just break the rules, they rewrote them. They didn't just reject society, they reinvented it. Pirates didn't just challenge the status-quo, they changed everyfuckingthing. Pirates faced a self-interested establishment, a broken system, industrial scale disruption and an uncertain future. Sound familiar? Pirates stood for MISCHIEF, PURPOSE and POWER. And you can too. In Be More Pirate, Sam Conniff Allende unveils the innovative strategies of Golden Age pirates, drawing parallels between the tactics and teachings of legends like Henry Morgan and Blackbeard with modern rebels, like Elon Musk, Malala and Banksy. Featuring takeaway sections and a guide to build you own pirate code 2.0, Be More Pirate will show you how to leave your mark on the 21st century. So what are you waiting for? Join the rebellion now. ----- 'Unique...reminds me of the fun we've had with our airlines' - Sir Richard Branson 'Totally compelling' Ed Miliband 'I'd rather be a pirate than join the navy' Steve Jobs 'A model for how to break the system and create radical change' Evening Standard 'Be More Pirate feels so important as it looks to history to help us grip the future' Martha Lane Fox 'This isn't a book, it's the beginning of a movement. Be More Pirate should come with a health warning' Tom Goodwin, author of Digital Darwinism

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.