Social Theories of Risk and Uncertainty

Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.

Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences. Uncertainty in Risk Assessment: Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. Offers guidance on when to use probability and when to use an alternative representation of uncertainty. Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. Uses examples to clearly illustrate ideas and concepts.

Inside the risk management and corporate governance issues behind capital structure decisions Practical ways of determining capital structures have always been mysterious and riddled with risks and uncertainties. Dynamic paradigm shifts and the multi-dimensional operations of firms further complicate the situation. Financial leaders are under constant pressure to outdo their competitors, but how to do so is not always clear. Capital Structure Decisions offers an introduction to corporate finance, and provides valuable insights into the decision-making processes that face the CEOs and CFOs of organizations in dynamic multi-objective environments. Exploring the various models and techniques used to understand the capital structure of an organization, as well as the products and means available for financing these structures, the book covers how to develop a goal programming model to enable organization leaders to make better capital structure decisions. Incorporating international case studies to explain various financial models and to illustrate ways that capital structure choices determine their success, Capital Structure Decisions looks at existing models and the development of a new goal-programming model for capital structures that is capable of handling multiple objectives, with an emphasis throughout on mitigating risk. Helps financial leaders understand corporate finance and the decision-making processes involved in understanding and developing capital structure Includes case studies from around the world that explain key financial models Emphasizes ways to minimize risk when it comes to working with capital structures There are a number of criteria that financial leaders need to consider before making any major capital investment decision. Capital Structure Decisions analyzes the various risk management and corporate governance issues to be considered by any diligent CEO/CFO before approving a project.

Theories in Social Psychology is an edited volume that identifies and discusses in-depth the important theoretical perspectives and theories that underlie the discipline of social psychology. The only current book focusing specifically on the theories within social psychology Brings together a range of distinguished scholars in the field of social psychology – including Bertram F. Malle, Paul R. Nail, Richard E. Petty, Thomas Mussweiler, Faye J. Crosby, Miles Hewstone, Richard J. Crisp and Mein Koslowsky Critically discusses important perspectives and theories in the discipline allowing a deeper understanding of the theoretical framework Allows students and academics to reflect on theories and opens up future areas of enquiry

Extremism and the Psychology of Uncertainty showcases cutting-edge scientific research on the extent to which uncertainty may lead to extremism. Contributions come from leading international scholars who focus on a wide variety of forms, facets and manifestations of extremist behavior. Systematically integrates and explores the growing diversity of social psychological perspectives on the uncertainty extremism relationship Showcases contemporary cutting edge scientific research from leading international scholars Offers a broad perspective on extremism and focuses on a wide variety of different forms, facets and manifestations Accessible to social and behavioral scientists, policy makers and those with a genuine interest in understanding the psychology of extremism

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ? Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the “black-box” view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making. Modelling Under Risk and Uncertainty: Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems. Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events. Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis. Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition. Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding. Supports Master/PhD-level course as well as advanced tutorials for professional training Analysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.

Deal with information and uncertainty properly and efficiently using tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized Information Theory contains comprehensive and up-to-date coverage of results that have emerged from a research program begun by the author in the early 1990s under the name «generalized information theory» (GIT). This ongoing research program aims to develop a formal mathematical treatment of the interrelated concepts of uncertainty and information in all their varieties. In GIT, as in classical information theory, uncertainty (predictive, retrodictive, diagnostic, prescriptive, and the like) is viewed as a manifestation of information deficiency, while information is viewed as anything capable of reducing the uncertainty. A broad conceptual framework for GIT is obtained by expanding the formalized language of classical set theory to include more expressive formalized languages based on fuzzy sets of various types, and by expanding classical theory of additive measures to include more expressive non-additive measures of various types. This landmark book examines each of several theories for dealing with particular types of uncertainty at the following four levels: * Mathematical formalization of the conceived type of uncertainty * Calculus for manipulating this particular type of uncertainty * Justifiable ways of measuring the amount of uncertainty in any situation formalizable in the theory * Methodological aspects of the theory With extensive use of examples and illustrations to clarify complex material and demonstrate practical applications, generous historical and bibliographical notes, end-of-chapter exercises to test readers' newfound knowledge, glossaries, and an Instructor's Manual, this is an excellent graduate-level textbook, as well as an outstanding reference for researchers and practitioners who deal with the various problems involving uncertainty and information. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.

Gain a deeper understanding of the issues surrounding financial risk and regulation Foundations of Financial Risk details the various risks, regulations, and supervisory requirements institutions face in today's economic and regulatory environment. Written by the experts at the Global Association of Risk Professionals (GARP), this book represents an update to GARP's original publication, Foundations of Banking Risk. You'll learn the terminology and basic concepts surrounding global financial risk and regulation, and develop an understanding of the methods used to measure and manage market, credit, and operational risk. Coverage includes traded market risk and regulation, treasury risk and regulation, and much more, including brand new coverage of risk management for insurance companies. Clear explanations, focused discussion, and comprehensive relevancy make this book an ideal resource for an introduction to risk management. The textbook provides an understanding of risk management methodologies, governance structures for risk management in financial institutions and the regulatory requirements dictated by the Basel Committee on Banking Supervision. It provides thorough coverage of the issues surrounding financial risk, giving you a solid knowledgebase and a practical, applicable understanding. Understand risk measurement and management Learn how minimum capital requirements are regulated Explore all aspects of financial institution regulation and disclosure Master the terminology of global risk and regulation Financial institutions and supervisors around the world are increasingly recognizing how vital sound risk management practices are to both individual firms and the capital markets system as a whole. Savvy professionals recognize the need for authoritative and comprehensive training, and Foundations of Financial Risk delivers with expert-led education for those new to risk management.

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: • Brings together the perspectives of researchers in areas of inverse problems and data assimilation. • Assesses the current state-of-the-art and identify needs and opportunities for future research. • Focuses on the computational methods used to analyze and simulate inverse problems. • Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.