Presents systems-based theory, methodology, and applications in risk modeling, assessment, and management This book examines risk analysis, focusing on quantifying risk and constructing probabilities for real-world decision-making, including engineering, design, technology, institutions, organizations, and policy. The author presents fundamental concepts (hierarchical holographic modeling; state space; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision trees; multi-objective risk impact analysis method; guiding principles in risk analysis); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies. The book will be used in systems engineering, enterprise risk management, engineering management, industrial engineering, civil engineering, and operations research. The fourth edition of Risk Modeling, Assessment, and Management features: Expanded chapters on systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling interdependent and interconnected complex systems of systems with phantom system models; and hierarchical holographic modeling An expanded appendix including a Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer’s Dilemma formulated and solved using a deterministic linear model Updated case studies including a new case study on sequential Pareto-optimal decisions for emergent complex systems of systems A new companion website with over 200 solved exercises that feature risk analysis theories, methodologies, and application Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more.

Risk or uncertainty assessments are used as aids to decision making in nearly every aspect of business, education, and government. As a follow-up to the author's bestselling Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Risk Modeling for Determining Value and Decision Making presents comprehensive examples of risk

Everyday we face decisions that carry an element of risk and uncertainty. The ability to analyse, communicate and control the level of risk entailed by these decisions remains one of the most pressing challenges to the analyst, scientist and manager. This book presents the foundational issues in risk analysis ? expressing risk, understanding what risk means, building risk models, addressing uncertainty, and applying probability models to real problems. The principal aim of the book is to give the reader the knowledge and basic thinking they require to approach risk and uncertainty to support decision making. Presents a statistical framework for dealing with risk and uncertainty. Includes detailed coverage of building and applying risk models and methods. Offers new perspectives on risk, risk assessment and the use of parametric probability models. Highlights a number of applications from business and industry. Adopts a conceptual approach based on elementary probability calculus and statistical theory. Foundations of Risk Analysis provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty.

Risk or uncertainty assessments are used as aids to decision making in nearly every aspect of business, education, and government. As a follow-up to the author's bestselling Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Risk Modeling for Determining Value and Decision Making presents comprehensive examples of risk/uncertainty analyses from a broad range of applications. Decision/option selection Manufacturing Environmental assessment Pricing Identification of business drivers Production sharing Insurance Scheduling and optimization Investing Security Law Emphasizing value as the focus of risk assessment, this book offers discussions on how to make decisions using each risk model and what insights the model can provide. The presentation of each model also includes computer code that encapsulates its logic and direction on how to apply the model to other types of problems. The author devotes a chapter to techniques for consistently collecting data in an inconsistent world and offers another chapter on how to reflect the effect of "soft" issues in the value of an opportunity. The book's final chapters delineate the techniques and technologies used to perform risk/uncertainty analyses, including sections on distribution, Monte Carlo process, dependence, sensitivity analysis, time series analysis, and chance of failure. Visit RiskSupport.com for more information!

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques. The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody’s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting.

Risk Monetization: Converting Threats and Opportunities into Impact on Project Value addresses the organizational, political, cultural, and technical issues related to implementing a successful risk assessment, management, and monetization process. Suitable for readers in any organization or area of expertise, the book assumes no prior background i

"This book focuses on the technical planning of power systems, taking into account technological evolutions in equipment as well as the economic, financial, and societal factors that drive supply and demand and have implications for technical planning at the micro level"--Provided by publisher.

Primer on Risk Analysis: Decision Making Under Uncertainty, Second Edition lays out the tasks of risk analysis in a straightforward, conceptual manner, tackling the question, "What is risk analysis?" Distilling the common principles of many risk dialects into serviceable definitions, it provides a foundation for the practice of risk management and decision making under uncertainty for professionals from all disciplines. New in this edition is an expanded risk management emphasis that includes an overview chapter on enterprise risk management and a chapter on decision making under uncertainty designed to help decision makers use the results of risk analysis in practical ways to improve decisions and their outcomes. This book will empower you to enter the world of risk management in your own domain of expertise by providing you with practical, insightful, useful and adaptable knowledge of risk analysis science including risk management, risk assessment, and risk communication. Features: Answers the fundamental question, "What is Risk Analysis?" Presents the tasks of risk management, risk assessment, and risk communication in a straightforward, conceptual manner Responds to the continuing evolution of risk science and addresses the language of risk as it continues to evolve Expands the risk management emphasis with a new chapter to serve private industry and a growing public sector interest in the growing practice of enterprise risk management Includes a new chapter on decision making under uncertainty provides practical guidance and ideas for using risk science to improve decisions and their outcomes Features an expanded set of examples of the risk process that demonstrate the growing applications of risk analysis This book is suitable for executives, professionals and students who seek a fundamental understanding of risk management, risk assessment, and risk communication. A more detailed examination of this topic, suitable for practitioners from any discipline as well as students and professionals who aspire to become experts in the practice of risk analysis science, is found in Principles of Risk Analysis: Decision Making Under Uncertainty, Second Edition, ISBN: 978-1-138-47820-6.

Written for safety and loss-control, environmental, and quality managers, this is the first comprehensive, integrated guide to developing a complete environmental risk analysis for regulated substances and processes. Unlike other books, Introduction to Risk Analysis looks at risk from a regulatory perspective, allowing both professionals in regulatory agencies concerned with risk--including OSHA, EPA, USDA, DOT, FDA, and state environmental agencies--and professionals in any agency-regulated industry to understand and implement the methods required for proper risk assessment. The authors examine risk and the structure of analysis. Emphasizing the predictive nature of risk, they discuss the quantitative nature of risk and explore quantitative-analysis topics, including data graphing, logarithmic thinking, risk estimating, and curve fitting. Chapters include discussions on functions, models, and uncertainties; the regulatory process; risk assessment; exposure; dosimetry; epidemiology; toxicology; risk characterization; comparative risk assessment; ecological risk assessment; risk management; and risk communication. Six in-depth case studies, an annotated bibliography, and more than 50 figures are also included.

System Health Management: with Aerospace Applications provides the first complete reference text for System Health Management (SHM), the set of technologies and processes used to improve system dependability. Edited by a team of engineers and consultants with SHM design, development, and research experience from NASA, industry, and academia, each heading up sections in their own areas of expertise and co-coordinating contributions from leading experts, the book collates together in one text the state-of-the-art in SHM research, technology, and applications. It has been written primarily as a reference text for practitioners, for those in related disciplines, and for graduate students in aerospace or systems engineering. There are many technologies involved in SHM and no single person can be an expert in all aspects of the discipline.System Health Management: with Aerospace Applications provides an introduction to the major technologies, issues, and references in these disparate but related SHM areas. Since SHM has evolved most rapidly in aerospace, the various applications described in this book are taken primarily from the aerospace industry. However, the theories, techniques, and technologies discussed are applicable to many engineering disciplines and application areas. Readers will find sections on the basic theories and concepts of SHM, how it is applied in the system life cycle (architecture, design, verification and validation, etc.), the most important methods used (reliability, quality assurance, diagnostics, prognostics, etc.), and how SHM is applied in operations (commercial aircraft, launch operations, logistics, etc.), to subsystems (electrical power, structures, flight controls, etc.) and to system applications (robotic spacecraft, tactical missiles, rotorcraft, etc.).