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| decision tree in risk management: Decision Trees for Decision Making John F. Magee, 1964 |
| decision tree in risk management: Confronting Climate Uncertainty in Water Resources Planning and Project Design Patrick A. Ray, Casey M. Brown, 2015-08-20 Confronting Climate Uncertainty in Water Resources Planning and Project Design describes an approach to facing two fundamental and unavoidable issues brought about by climate change uncertainty in water resources planning and project design. The first is a risk assessment problem. The second relates to risk management. This book provides background on the risks relevant in water systems planning, the different approaches to scenario definition in water system planning, and an introduction to the decision-scaling methodology upon which the decision tree is based. The decision tree is described as a scientifically defensible, repeatable, direct and clear method for demonstrating the robustness of a project to climate change. While applicable to all water resources projects, it allocates effort to projects in a way that is consistent with their potential sensitivity to climate risk. The process was designed to be hierarchical, with different stages or phases of analysis triggered based on the findings of the previous phase. An application example is provided followed by a descriptions of some of the tools available for decision making under uncertainty and methods available for climate risk management. The tool was designed for the World Bank but can be applicable in other scenarios where similar challenges arise. |
| decision tree in risk management: Quantitative Risk Management and Decision Making in Construction Amarjit Singh, 2017 Singh introduces valuable techniques for weighing and evaluating alternatives in decision making with a focus on risk analysis for identifying, quantifying, and mitigating risks associated with construction projects. |
| decision tree in risk management: Risk and Decision Analysis in Projects John R. Schuyler, 2001 Some of Schuyler's tried-and-true tips include: - The single-point estimate is almost always wrong, so that it is always better to express judgments as ranges. A probability distribution completely expresses someone's judgment about the likelihood of values within the range.- We often need a single-value cost or other assessment, and the expected value (mean) of the distribution is the only unbiased predictor. Expected value is the probability-weighted average, and this statistical idea is the cornerstone of decision analysis.- Some decisions are easy, perhaps aided by quick decision tree calculations on the back of an envelope. Decision dilemmas typically involve risky outcomes, many factors, and the best alternatives having comparable value. We only need analysis sufficient to confidently identify the best alternative. As soon as you know what to do, stop the analysis!- Be alert to ways to beneficially change project risks. We can often eliminate, avoid, transfer, or mitigate threats in some way. Get to know the people who make their living helping managers sidestep risk. They include insurance agents, partners, turnkey contractors, accountants, trainers, and safety personnel. |
| decision tree in risk management: Statistics and Probability Theory Michael Havbro Faber, 2012-03-26 This book provides the reader with the basic skills and tools of statistics and probability in the context of engineering modeling and analysis. The emphasis is on the application and the reasoning behind the application of these skills and tools for the purpose of enhancing decision making in engineering. The purpose of the book is to ensure that the reader will acquire the required theoretical basis and technical skills such as to feel comfortable with the theory of basic statistics and probability. Moreover, in this book, as opposed to many standard books on the same subject, the perspective is to focus on the use of the theory for the purpose of engineering model building and decision making. This work is suitable for readers with little or no prior knowledge on the subject of statistics and probability. |
| decision tree in risk management: Risk Assessment and Decision Making in Business and Industry Glenn Koller, 2005-03-30 Building upon the technical and organizational groundwork presented in the first edition, Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Second Edition addresses the many aspects of risk/uncertainty (R/U) process implementation. This comprehensive volume covers four broad aspects of R/U: general concepts, i |
| decision tree in risk management: Advances in Patient Safety Kerm Henriksen, 2005 v. 1. Research findings -- v. 2. Concepts and methodology -- v. 3. Implementation issues -- v. 4. Programs, tools and products. |
| decision tree in risk management: Ethnographic Decision Tree Modeling Christina H. Gladwin, 1989-09 Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions. |
| decision tree in risk management: Data Mining with Decision Trees Lior Rokach, Oded Z. Maimon, 2008 This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection |
| decision tree in risk management: Risk Management in Engineering and Construction Stephen Ogunlana, Prasanta Kumar Dey, 2019-09-09 Today’s businesses are driven by customer ‘pull’ and technological ‘push’. To remain competitive in this dynamic business world, engineering and construction organizations are constantly innovating with new technology tools and techniques to improve process performance in their projects. Their management challenge is to save time, reduce cost and increase quality and operational efficiency. Risk management has recently evolved as an effective method of managing both projects and operations. Risk is inherent in any project, as managers need to plan projects with minimal knowledge and information, but its management helps managers to become proactive rather than reactive. Hence, it not only increases the chance of project achievement, but also helps ensure better performance throughout its operations phase. Various qualitative and quantitative tools are researched extensively by academics and routinely deployed by practitioners for managing risk. These have tremendous potential for wider applications. Yet the current literature on both the theory and practice of risk management is widely scattered. Most of the books emphasize risk management theory but lack practical demonstrations and give little guidance on the application of those theories. This book showcases a number of effective applications of risk management tools and techniques across product and service life in a way useful for practitioners, graduate students and researchers. It also provides an in-depth understanding of the principles of risk management in engineering and construction. |
| decision tree in risk management: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| decision tree in risk management: Identifying and Managing Project Risk Tom Kendrick, 2009-02-27 Winner of the Project Management Institute’s David I. Cleland Project Management Literature Award 2010 It’s no wonder that project managers spend so much time focusing their attention on risk identification. Important projects tend to be time constrained, pose huge technical challenges, and suffer from a lack of adequate resources. Identifying and Managing Project Risk, now updated and consistent with the very latest Project Management Body of Knowledge (PMBOK)® Guide, takes readers through every phase of a project, showing them how to consider the possible risks involved at every point in the process. Drawing on real-world situations and hundreds of examples, the book outlines proven methods, demonstrating key ideas for project risk planning and showing how to use high-level risk assessment tools. Analyzing aspects such as available resources, project scope, and scheduling, this new edition also explores the growing area of Enterprise Risk Management. Comprehensive and completely up-to-date, this book helps readers determine risk factors thoroughly and decisively...before a project gets derailed. |
| decision tree in risk management: Project Risk Analysis and Management Guide John Bartlett, 2004 The second edition of the Project Risk Analysis and Management Guide maintains the flavour of the original and the qualities that made the first edition so successful. The new edition includes: The latest practices and approaches to risk management in projects; Coverage of project risk in its broadest sense, as well as individual risk events; The use of risk management to address opportunities (uncertain events with a positive effect on the project's objectives); A comprehensive description of the tools and techniques required; New material on the human factors, organisational issues and the requirements of corporate governance; New chapters on the benefits and also behavioural issues |
| decision tree in risk management: Urban Tree Risk Management , 2003 |
| decision tree in risk management: Lean CX Robert Dew, Bill Russell, Cyrus Allen, George Bej, 2021-04-06 In recent years, many companies have realised customer experience (CX) is the new marketing battle ground. Substantial investments have been made to map customer journeys, identify pain points and improve CX to try and create cut-through. Using real world applications to introduce next generation design tools based on proven concepts from strategy, marketing, psychology and creative problem solving, Lean CX: How to Differentiate at Low Cost and Least Risk discusses how to use Lean Management approaches to innovate your customer experience. This practical book describes how the tools from Lean Management can be applied to the CX innovation problem. The authors draw on hundreds of CX design and strategic innovation projects across a range of industries, both B2B and B2C, from primary research through client work and secondary case studies available in the public domain. The examples include many different vertical industry sectors, including those involving hybrid business models. The cases included share what worked really well and where CX failed. The content goes beyond what actually happened to present an idea of what might be possible with the right design approach and committed resources. |
| decision tree in risk management: Risk Modeling, Assessment, and Management Yacov Y. Haimes, 2011-09-20 Examines timely multidisciplinary applications, problems, and case histories in risk modeling, assessment, and management Risk Modeling, Assessment, and Management, Third Edition describes the state of the art of risk analysis, a rapidly growing field with important applications in engineering, science, manufacturing, business, homeland security, management, and public policy. Unlike any other text on the subject, this definitive work applies the art and science of risk analysis to current and emergent engineering and socioeconomic problems. It clearly demonstrates how to quantify risk and construct probabilities for real-world decision-making problems, including a host of institutional, organizational, and political issues. Avoiding higher mathematics whenever possible, this important new edition presents basic concepts as well as advanced material. It incorporates numerous examples and case studies to illustrate the analytical methods under discussion and features restructured and updated chapters, as well as: A new chapter applying systems-driven and risk-based analysis to a variety of Homeland Security issues An accompanying FTP site—developed with Professor Joost Santos—that offers 150 example problems with an Instructor's Solution Manual and case studies from a variety of journals Case studies on the 9/11 attack and Hurricane Katrina An adaptive multiplayer Hierarchical Holographic Modeling (HHM) game added to Chapter Three This is an indispensable resource for academic, industry, and government professionals in such diverse areas as homeland and cyber security, healthcare, the environment, physical infrastructure systems, engineering, business, and more. It is also a valuable textbook for both undergraduate and graduate students in systems engineering and systems management courses with a focus on our uncertain world. |
| decision tree in risk management: Project Risk Management Yuri Raydugin, 2013-09-10 An easy to implement, practical, and proven risk management methodology for project managers and decision makers Drawing from the author's work with several major and mega capital projects for Royal Dutch Shell, TransCanada Pipelines, TransAlta, Access Pipeline, MEG Energy, and SNC-Lavalin, Project Risk Management: Essential Methods for Project Teams and Decision Makers reveals how to implement a consistent application of risk methods, including probabilistic methods. It is based on proven training materials, models, and tools developed by the author to make risk management plans accessible and easily implemented. Written by an experienced risk management professional Reveals essential risk management methods for project teams and decision makers Packed with training materials, models, and tools for project management professionals Risk Management has been identified as one of the nine content areas for Project Management Professional (PMP®) certification. Yet, it remains an area that can get bogged down in the real world of project management. Practical and clearly written, Project Risk Management: Essential Methods for Project Teams and Decision Makers equips project managers and decision makers with a practical understanding of the basics of risk management as they apply to project management. (PMP and Project Management Professional are registered marks of the Project Management Institute, Inc.) |
| decision tree in risk management: The Lean CX Score David McLachlan, 2017-09-11 The Lean CX Score is a brand new repeatable framework to help you create disruptive products and services. |
| decision tree in risk management: Decision Analysis for Management Judgment Paul Goodwin, George Wright, 2014-05-12 Decision Analysis for Management Judgment is unique in its breadth of coverage of decision analysis methods. It covers both the psychological problems that are associated with unaided managerial decision making and the decision analysis methods designed to overcome them. It is presented and explained in a clear, straightforward manner without using mathematical notation. This latest edition has been fully revised and updated and includes a number of changes to reflect the latest developments in the field. |
| decision tree in risk management: ProjectThink Lev Virine, Michael Trumper, 2016-04-15 Projects are constantly beset by problems, often caused by seemingly small mistakes which collectively lead to larger issues. Why do project managers and teams appear to repeat the same mistakes? Can they make better choices without introducing complex decision analysis processes? How can they make better estimates? Project management is the art and science of human interactions. ProjectThink identifies and explains the paths of those intentional and unintentional actions that lead to trouble. It provides advice and guidance in analysing information and risk and explains how ’choice-engineering’ can facilitate decision-making and encourage everyone involved in a project to follow the right procedures and work collaboratively. |
| decision tree in risk management: Strategic Risk Taking Aswath Damodaran, 2008 Groundbreaking book that redefines risk in business as potentially powerful strategically to help increase profits. bull; Get out of your defensive crouch : learn which risks to avoid, which to mitigate, and which to actively exploit. bull; Master risk management techniques that can drive competitive advantage, increase firm value, and enhance growth and profitability. bull; By Dr. Aswath Damodaran, one of the field's top gurus - known worldwide for his classic guides to corporate finance and valuation. |
| decision tree in risk management: The Analytic Hierarchy Process Bruce L. Golden, Edward A. Wasil, Patrick T. Harker, 2012-12-06 Management science is a di scipl ine dedicated to the development of techniques that enable decision makers to cope with the increasing complexity of our world. The early burst of excitement which was spawned by the development and successful applications of linear programming to problems in both the public and private sectors has challenged researchers to develop even more sophisticated methods to deal with the complex nature of decision making. Sophistication, however, does not always trans 1 ate into more complex mathematics. Professor Thomas L. Saaty was working for the U. S. Defense Department and for the U. S. Department of State in the late 1960s and early 1970s. In these positions, Professor Saaty was exposed to some of the most complex decisions facing the world: arms control, the Middle East problem, and the development of a transport system for a Third World country. While having made major contributions to numerous areas of mathematics and the theory of operations research, he soon realized that one did not need complex mathematics to come to grips with these decision problems, just the right mathematics! Thus, Professor Saaty set out to develop a mathematically-based technique for analyzing complex situations which was sophisticated in its simplicity. This technique became known as the Analytic Hierarchy Process (AHP) and has become very successful in helping decision makers to structure and analyze a wide range of problems. |
| decision tree in risk management: Presto Sketching Ben Crothers, 2017-10-19 Do you feel like your thoughts, ideas, and plans are being suffocated by a constant onslaught of information? Do you want to get those great ideas out of your head, onto the whiteboard and into everyone else’s heads, but find it hard to start? No matter what level of sketching you think you have, Presto Sketching will help you lift your game in visual thinking and visual communication. In this practical workbook, Ben Crothers provides loads of tips, templates, and exercises that help you develop your visual vocabulary and sketching skills to clearly express and communicate your ideas. Learn techniques like product sketching, storyboarding, journey mapping, and conceptual illustration. Dive into how to use a visual metaphor (with a library of 101 visual metaphors), as well as tips for capturing and sharing your sketches digitally, and developing your own style. Designers, product managers, trainers, and entrepreneurs will learn better ways to explore problems, explain concepts, and come up with well-defined ideas - and have fun doing it. |
| decision tree in risk management: Risk Management Carl L. Pritchard, 1997 |
| decision tree in risk management: Simple Tools and Techniques for Enterprise Risk Management Robert J. Chapman, 2011-12-12 Your business reputation can take years to build—and mere minutes to destroy The range of business threats is evolving rapidly but your organization can thrive and gain a competitive advantage with your business vision for enterprise risk management. Trends affecting markets—events in the global financial markets, changing technologies, environmental priorities, dependency on intellectual property—all underline how important it is to keep up to speed on the latest financial risk management practices and procedures. This popular book on enterprise risk management has been expanded and updated to include new themes and current trends for today's risk practitioner. It features up-to-date materials on new threats, lessons from the recent financial crisis, and how businesses need to protect themselves in terms of business interruption, security, project and reputational risk management. Project risk management is now a mature discipline with an international standard for its implementation. This book reinforces that project risk management needs to be systematic, but also that it must be embedded to become part of an organization's DNA. This book promotes techniques that will help you implement a methodical and broad approach to risk management. The author is a well-known expert and boasts a wealth of experience in project and enterprise risk management Easy-to-navigate structure breaks down the risk management process into stages to aid implementation Examines the external influences that bring sources of business risk that are beyond your control Provides a handy chapter with tips for commissioning consultants for business risk management services It is a business imperative to have a clear vision for risk management. Simple Tools and Techniques for Enterprise Risk Management, Second Edition shows you the way. |
| decision tree in risk management: COSO Enterprise Risk Management Robert R. Moeller, 2011-07-26 A fully updated, step-by-step guide for implementing COSO's Enterprise Risk Management COSO Enterprise Risk Management, Second Edition clearly enables organizations of all types and sizes to understand and better manage their risk environments and make better decisions through use of the COSO ERM framework. The Second Edition discusses the latest trends and pronouncements that have affected COSO ERM and explores new topics, including the PCAOB's release of AS5; ISACA's recently revised CobiT; and the recently released IIA Standards. Offers you expert advice on how to carry out internal control responsibilities more efficiently Updates you on the ins and outs of the COSO Report and its emergence as the new platform for understanding all aspects of risk in today's organization Shows you how an effective risk management program, following COSO ERM, can help your organization to better comply with the Sarbanes-Oxley Act Knowledgeably explains how to implement an effective ERM program Preparing professionals develop and follow an effective risk culture, COSO Enterprise Risk Management, Second Edition is the fully revised, invaluable working resource that will show you how to identify risks, avoid pitfalls within your corporation, and keep it moving ahead of the competition. |
| decision tree in risk management: Risk Assessment and Decision Analysis with Bayesian Networks Norman Fenton, Martin Neil, 2018-09-03 Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course. |
| decision tree in risk management: The Owner's Role in Project Risk Management National Research Council, Division on Engineering and Physical Sciences, Board on Infrastructure and the Constructed Environment, Committee for Oversight and Assessment of U.S. Department of Energy Project Management, 2005-02-25 Effective risk management is essential for the success of large projects built and operated by the Department of Energy (DOE), particularly for the one-of-a-kind projects that characterize much of its mission. To enhance DOE's risk management efforts, the department asked the NRC to prepare a summary of the most effective practices used by leading owner organizations. The study's primary objective was to provide DOE project managers with a basic understanding of both the project owner's risk management role and effective oversight of those risk management activities delegated to contractors. |
| decision tree in risk management: Reliability and Risk Nozer D. Singpurwalla, 2006-08-14 We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications. Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by: Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis. Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book. Introducing the notion of “composite reliability”, or the collective reliability of a population of items. Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk. Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems. |
| decision tree in risk management: Sustainable Construction Engineering and Management Edmundas Kazimieras Zavadskas, Jurgita Antuchevičienė, M. Reza Hosseini, Igor Martek, 2021 This Book is a Printed Edition of the Special Issue which covers sustainability as an emerging requirement in the fields of construction management, project management and engineering. We invited authors to submit their theoretical or experimental research articles that address the challenges and opportunities for sustainable construction in all its facets, including technical topics and specific operational or procedural solutions, as well as strategic approaches aimed at the project, company or industry level. Central to developments are smart technologies and sophisticated decision-making mechanisms that augment sustainable outcomes. The Special Issue was received with great interest by the research community and attracted a high number of submissions. The selection process sought to balance the inclusion of a broad representative spread of topics against research quality, with editors and reviewers settling on thirty-three articles for publication. The Editors invite all participating researchers and those interested in sustainable construction engineering and management to read the summary of the Special Issue and of course to access the full-text articles provided in the Book for deeper analyses. |
| decision tree in risk management: People Risk Management Keith Blacker, Patrick McConnell, 2015-04-03 People Risk Management provides unique depth to a topic that has garnered intense interest in recent years. Based on the latest thinking in corporate governance, behavioural economics, human resources and operational risk, people risk can be defined as the risk that people do not follow the organization's procedures, practices and/or rules, thus deviating from expected behaviour in a way that could damage the business's performance and reputation. From fraud to bad business decisions, illegal activity to lax corporate governance, people risk - often called conduct risk - presents a growing challenge in today's complex, dispersed business organizations. Framed by corporate events and challenges and including case studies from the LIBOR rate scandal, the BP oil spill, Lehman Brothers, Royal Bank of Scotland and Enron, People Risk Management provides best-practice guidance to managing risks associated with the behaviour of both employees and those outside a company. It offers practical tools, real-world examples, solutions and insights into how to implement an effective people risk management framework within an organization. |
| decision tree in risk management: Handbook of Integrated Risk Management for E-Business Abderrahim Labbi, 2005-11-09 “This book provides a recipe for the practical application of technology and is one of the first instances where the tools and technologies that allow for the implementation of solutions to solve specific problems are actually outlined.” --Dr. Krishna Nathan, Vice President, IBM Research This ground-breaking book integrates converging views of e-business processes and offers ways to manage their inherent risks with advanced modeling techniques. Contributors from leading academic and business organizations explore state-of-the-art adaptive risk analysis systems that support business processes in project portfolio management, operations management, supply chain management, inventory control, data mining for customer relationship management, information technology security, finance, e-banking, and more. Today’s new business environments are characterized by increasing sources of uncertainty and variability which challenge current decision-making processes.Handbook of Integrated Risk Management for E-Business: Measuring, Modeling, and Managing Risk provides a roadmap for identifying and mitigating the primary risks associated with each critical e-business process. It also shows you how to transform your processes by empowering your decision-making systems and how to design appropriate risk management systems for decision support. |
| decision tree in risk management: Applied Software Risk Management C. Ravindranath Pandian, 2006-12-15 Few software projects are completed on time, on budget, and to their original specifications. Focusing on what practitioners need to know about risk in the pursuit of delivering software projects, Applied Software Risk Management: A Guide for Software Project Managers covers key components of the risk management process and the software development |
| decision tree in risk management: Theory and Practice in Policy Analysis M. Granger Morgan, 2017-10-12 Many books instruct readers on how to use the tools of policy analysis. This book is different. Its primary focus is on helping readers to look critically at the strengths, limitations, and the underlying assumptions analysts make when they use standard tools or problem framings. Using examples, many of which involve issues in science and technology, the book exposes readers to some of the critical issues of taste, professional responsibility, ethics, and values that are associated with policy analysis and research. Topics covered include policy problems formulated in terms of utility maximization such as benefit-cost, decision, and multi-attribute analysis, issues in the valuation of intangibles, uncertainty in policy analysis, selected topics in risk analysis and communication, limitations and alternatives to the paradigm of utility maximization, issues in behavioral decision theory, issues related to organizations and multiple agents, and selected topics in policy advice and policy analysis for government. |
| decision tree in risk management: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| decision tree in risk management: The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy John Macintyre, Jinghua Zhao, Xiaomeng Ma, 2021-11-02 This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field. |
| decision tree in risk management: Systemic and Systematic Risk Management Joseph E. Kasser, 2020-05-14 This book discusses risk management as it applies to problem-solving for simple, complex and wicked problems faced by policy creators and implementors, project managers and systems engineers in the context of policies, large engineering projects (LEPs), projects and systems. When applying systems thinking to risk management, it can be seen that risk management applies to almost every action taken in daily life. This book: Introduces the systems approach of integrating risk management into policy creation and implementation, project management and systems engineering, such as the risk framework and the Firm Fixed Price (FFP) contract with penalties and bonuses. Introduces a number of out-of-the box concepts building on the application of the systems thinking tools in the system thinker’s toolbox. Points out that integrating risk management into policy and project management and systems engineering is just good management and engineering practice. Discusses the flow of risk in a policy from creation through implementation via LEPs and simpler projects, identifying where risks arise and where they should be dealt with. Presents the risks in the relationship between policy creation, implementation, project management and systems engineering. Discusses risks throughout the policy implementation process and shows how the nature of risks changes from political to financial to technological as implementation proceeds. Discusses managing complexity and specifies the minimum number of elements in a system for it to be defined as, and managed as, complex. Points out that in most instances the traditionally ignored major implementation risk is that of poor performance by personnel. Shows how to proactively incorporate prevention into planning in order to prevent risks, as well as how to mitigate them when they occur. |
| decision tree in risk management: Principles of Risk-Based Decision Making In c. ABS Consulting, 2002-02 Principles of Risk-Based Decision Making provides managers with the foundation for creating a proactive organizational culture that systematically incorporates risk into key decision-making processes. Based on methodology adopted by a number of organizations including the federal government, this book examines risk-based decision making as a process for organizing information about the possibility for unwanted outcomes in a simple, practical way that helps decision makers make timely, informed management choices that minimize harmful effects on safety and health, the environment, property loss, or mission success. Citing practical examples, charts, and checklists, the authors break the risk-based decision making process into five key components: establishing the decision structure, performing the risk assessment, managing sufficient risks, monitoring effectiveness of adopted risk controls through impact assessment, and facilitating risk communication. They examine each component in detail and outline available decision analysis and risk assessment tools that aid in each of these risk-based decision making functions. This book also walks readers through eight project management steps—from scoping a risk assessment to evaluating the recommendations—the components of each, and the importance of these steps to the success of a risk assessment. Special features include a table for applying the risk-based decision-making process, a hazard identification guidesheet, an example of human error, an acronym list, and a glossary. |
| decision tree in risk management: Risk Management Carl L. Pritchard, PMP, PMI-RMP, EVP, 2014-12-17 This new edition of Risk Management: Concepts and Guidance supplies a look at risk in light of current information, yet remains grounded in the history of risk practice. Taking a holistic approach, it examines risk as a blend of environmental, programmatic, and situational concerns. Supplying comprehensive coverage of risk management tools, practices, and protocols, the book presents powerful techniques that can enhance organizational risk identification, assessment, and management—all within the project and program environments. Updated to reflect the Project Management Institute’s A Guide to the Project Management Body of Knowledge (PMBOK® Guide), Fifth Edition, this edition is an ideal resource for those seeking Project Management Professional and Risk Management Professional certification. Emphasizing greater clarity on risk practice, this edition maintains a focus on the ability to apply planned clairvoyance to peer into the future. The book begins by analyzing the various systems that can be used to apply risk management. It provides a fundamental introduction to the basics associated with particular techniques, clarifying the essential concepts of risk and how they apply in projects. The second part of the book presents the specific techniques necessary to successfully implement the systems described in Part I. The text addresses project risk management from the project manager’s perspective. It adopts PMI’s perspective that risk is both a threat and an opportunity, and it acknowledges that any effective risk management practice must look at the potential positive events that may befall a project, as well as the negatives. Providing coverage of the concepts that many project management texts ignore, such as the risk response matrix and risk models, the book includes appendices filled with additional reference materials and supporting details that simplifying some of the most complex aspects of risk management. |
| decision tree in risk management: Value of Information in the Earth Sciences Jo Eidsvik, Tapan Mukerji, Debarun Bhattacharjya, 2015-11-19 Gathering the right kind and the right amount of information is crucial for any decision-making process. This book presents a unified framework for assessing the value of potential data gathering schemes by integrating spatial modelling and decision analysis, with a focus on the Earth sciences. The authors discuss the value of imperfect versus perfect information, and the value of total versus partial information, where only subsets of the data are acquired. Concepts are illustrated using a suite of quantitative tools from decision analysis, such as decision trees and influence diagrams, as well as models for continuous and discrete dependent spatial variables, including Bayesian networks, Markov random fields, Gaussian processes, and multiple-point geostatistics. Unique in scope, this book is of interest to students, researchers and industry professionals in the Earth and environmental sciences, who use applied statistics and decision analysis techniques, and particularly to those working in petroleum, mining, and environmental geoscience. |
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: 2. the…. Learn more.
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a judgment.. See examples of DECISION used in a sentence.
decision noun - Definition, pictures, pronunciation and usage …
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is decided; resolution. 4. a judgment, as one pronounced by a court. 5. the …
What does Decision mean? - Definitions.net
What does Decision mean? This dictionary definitions page includes all the possible meanings, example usage and translations of the word Decision. A choice or judgement. Firmness of …
decision - Wiktionary, the free dictionary
Jun 7, 2025 · (choice or judgment): Most often, to decide something is to make a decision; however, other possibilities exist as well. Many verbs used with destination or conclusion, such …
SUPREME COURT OF THE UNITED STATES
3 days ago · judgment” rule articulated by the Eighth Circuit in its 1982 decision in Monahan, in which the Eighth Circuit reasoned that to prove dis-crimination under the Rehabilitation Act in …
Decision-making - Wikipedia
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several …
Decision - Definition, Meaning & Synonyms - Vocabulary.com
To make a decision is to make up your mind about something. To act with decision is to proceed with determination, which might be a natural character trait.
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: 2. the…. Learn more.
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a judgment.. See examples of DECISION used in a sentence.
decision noun - Definition, pictures, pronunciation and usage …
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is decided; resolution. 4. a judgment, as one pronounced by a court. 5. the …
What does Decision mean? - Definitions.net
What does Decision mean? This dictionary definitions page includes all the possible meanings, example usage and translations of the word Decision. A choice or judgement. Firmness of …
decision - Wiktionary, the free dictionary
Jun 7, 2025 · (choice or judgment): Most often, to decide something is to make a decision; however, other possibilities exist as well. Many verbs used with destination or conclusion, such …
SUPREME COURT OF THE UNITED STATES
3 days ago · judgment” rule articulated by the Eighth Circuit in its 1982 decision in Monahan, in which the Eighth Circuit reasoned that to prove dis-crimination under the Rehabilitation Act in …
Decision-making - Wikipedia
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several …
Decision - Definition, Meaning & Synonyms - Vocabulary.com
To make a decision is to make up your mind about something. To act with decision is to proceed with determination, which might be a natural character trait.
