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Previously published by Palisade Corporation, 1999 under the title: Decision making under uncertainty with RISKOptimizer : a step-by-step guide with Microsoft Excel and Palisade's RISKOptimizer software.
In every decision context there are things we know and things we do not know. Risk analysis uses science and the best available evidence to assess what we know—and it is intentional in the way it addresses the importance of the things we don’t know. Principles of Risk Analysis: Decision Making Under Uncertainty lays out the tasks of risk analysis in a straightforward, conceptual manner that is consistent with the risk models of all communities of practice. It answers the questions "what is risk analysis?" and "how do I do this?" Distilling the common principles of the many risk tribes and dialects into serviceable definitions and narratives, the book provides a foundation for the practice of risk analysis and decision making under uncertainty for professionals from all walks of life. In the first part of the book, readers learn the language, models, and concepts of risk analysis and its three component tasks—risk management, assessment, and communication. The second part of the book supplies the tools, techniques, and methodologies to help readers apply the principles. From problem identification and brainstorming to model building and choosing a probability distribution, the author walks readers through the how-to of risk assessment. Addressing the critical task of risk communication, he explains how to present the results of assessments and how to develop effective messages. The book’s simple and straightforward style—based on the author’s decades of experience as a risk analyst, trainer, and educator—strips away the mysterious aura that often accompanies risk analysis. It describes the principles in a manner that empowers readers to begin the practice of risk analysis, to better understand and use the models and practice of their individual fields, and to gain access to the rich and sophisticated professional literature on risk analysis. Additional exercises as well as a free student version of the Palisade Corporation DecisionTools® Suite software and files used in the preparation of this book are available for download.
The progression of risk management techniques provides the crucial applications and benefits to all of society. By analyzing the current trends and techniques used to assess and mitigate risks, safer processes can be used for all professional fields, as well as society as a whole. Novel Six Sigma Approaches to Risk Assessment and Management is a vital scholarly resource that provides an in-depth examination on innovative Six Sigma methods for risk mitigation initiatives. Featuring an array of relevant topics such as project management, production scheduling, information systems security, and agricultural planning, this is an ideal reference book for professionals, academicians, students, and researchers interested in detailed research on recent advancements in the management of risk in all fields.
In today's fast-paced business environment, project managers face the daunting challenge of managing risk effectively amid uncertainty. Traditional project management methodologies often lag, leading to missed deadlines, cost overruns, and subpar outcomes. A comprehensive risk management framework is necessary for organizations to be protected from fate's whims, hindering their ability to achieve strategic objectives. The DMAIC Stochastic Method is a groundbreaking approach that combines Six Sigma principles with stochastic modeling to revolutionize project risk management. Novel Six Sigma DMAIC Approaches to Project Risk Assessment and Management is a guidebook for implementing the DMAIC Stochastic Method in project management. This innovative methodology provides a systematic way of identifying, assessing, and mitigating risks, ensuring that projects stay on track and deliver the desired results. By integrating deterministic and stochastic models, the DMAIC Stochastic Method offers a more holistic view of risk, enabling managers to make informed decisions and proactively address potential issues.
Ever-increasing attacks against individual and corporate finances over the past few decades prompt swift action from the realm of financial management. Advances in protection as well as techniques for controlling these disasters is instrumental for financial security and threat prevention. Six Sigma Improvements for Basel III and Solvency II in Financial Risk Management: Emerging Research and Opportunities explores the theoretical and practical aspects of Six Sigma DMAIC methods and tools to improve the financial risk management process and applications within finance, research and development, and software engineering. Featuring coverage on a broad range of topics such as controlling VAR, financial institution evaluations, and global limit systems, this book is ideally designed for financial managers, risk managers, researchers, and academics seeking current research on financial risk management to ensure that uncertainty does not affect, or at least has a minimal impact on, the achievement of goals within a financial institution.
Practical Spreadsheet Modeling Using @Risk provides a guide of how to construct applied decision analysis models in spreadsheets. The focus is on the use of Monte Carlo simulation to provide quantitative assessment of uncertainties and key risk drivers. The book presents numerous examples based on real data and relevant practical decisions in a variety of settings, including health care, transportation, finance, natural resources, technology, manufacturing, retail, and sports and entertainment. All examples involve decision problems where uncertainties make simulation modeling useful to obtain decision insights and explore alternative choices. Good spreadsheet modeling practices are highlighted. The book is suitable for graduate students or advanced undergraduates in business, public policy, health care administration, or any field amenable to simulation modeling of decision problems. The book is also useful for applied practitioners seeking to build or enhance their spreadsheet modeling skills. Features Step-by-step examples of spreadsheet modeling and risk analysis in a variety of fields Description of probabilistic methods, their theoretical foundations, and their practical application in a spreadsheet environment Extensive example models and exercises based on real data and relevant decision problems Comprehensive use of the @Risk software for simulation analysis, including a free one-year educational software license
Optimization in Practice with MATLAB® provides a unique approach to optimization education. It is accessible to both junior and senior undergraduate and graduate students, as well as industry practitioners. It provides a strongly practical perspective that allows the student to be ready to use optimization in the workplace. It covers traditional materials, as well as important topics previously unavailable in optimization books (e.g. numerical essentials - for successful optimization). Written with both the reader and the instructor in mind, Optimization in Practice with MATLAB® provides practical applications of real-world problems using MATLAB®, with a suite of practical examples and exercises that help the students link the theoretical, the analytical, and the computational in each chapter. Additionally, supporting MATLAB® m-files are available for download via www.cambridge.org.messac. Lastly, adopting instructors will receive a comprehensive solution manual with solution codes along with lectures in PowerPoint with animations for each chapter, and the text's unique flexibility enables instructors to structure one- or two-semester courses.
Technological tools have enhanced the available opportunities and activities in the realm of e-business. In organizations that support real-time business-critical operations, the proper use and maintenance of relevant technology is crucial. Maximizing Information System Availability Through Bayesian Belief Network Approaches: Emerging Research and Opportunities is a pivotal book that features the latest research perspectives on the implementation of effective information systems in business contexts. Highlighting relevant topics such as data security, investment viability, and operational risk management, this book is ideally designed for managers, professionals, academics, practitioners, and students interested in novel techniques for maintaining and measuring information system availability.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.