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For thirty years, the literature on decision-making and planning has been divided into two camps : work premised on rational models of choice and work designed to discredit such models. The sustained critic of fully rational decision-making theories has al ready a long history and a constant message to deliver : in practice, consequential decision-making hardly fulfills the canons of perfect rationality. There is also evidence that decision-making and planning are not unitary processes. Although the concept of "decision-making" connotes the idea of a single process, making a single choice involves a complex of processing tasks : structuring the problem, finding alternatives worth considering, deciding what information is relevant, assessing various consequences, and a variety of others. The aim of this volume is to bring together and try to inter relate some of the concepts and relevant knowledge from various disciplines concerned with one important aspect of this complex process : the management of uncertainty. It is hardly necessary to reiterate the case made by numerous authors about our changing and increasingly uncertain world. Suffice it to say here that it is uncertainty about the future, and in many cases about the past and the present also, which makes decision-making and planning so difficul t. The management of uncertainty may be defined as the way in which uncertainty is treated and processed in decision-making.
As I write, the financial systems of the world are collapsing with still no clear indication of what the consequences will be and which measures should be taken to avoid such a crisis in the future. There seems to be agreement though, that the financial instruments introduced in the past few decades entailed far too much complexity and uncertainty and that there was too little regulatory control over the use of these instruments. Management of uncertainty with the aim of achieving self-control is the core concern of this book. It was not written with a focus on financial systems, but many concepts developed in this book are applicable to this field as well. The - neric principles of reducing, maintaining or increasing uncertainties in view of the different contingencies an organization is faced with, the fundamental issue of how much control is possible and who should be in control, and the question of how much and what kind of regulation is necessary with the overall aim of finding an appropriate balance between system stability and flexibility are at the centre of heated debates on the future of finance.
This is the first book of its kind – explicitly considering uncertainty and error analysis as an integral part of scaling. The book draws together a series of important case studies to provide a comprehensive review and synthesis of the most recent concepts, theories and methods in scaling and uncertainty analysis. It includes case studies illustrating how scaling and uncertainty analysis are being conducted in ecology and environmental science.
Managing uncertainty has become a new business imperative. Technological discontinuities, regulatory upheavals, geopolitical shocks, abrupt shifts in consumer tastes or behavior, and many other factors have emerged or intensified in recent years and together conspire to undermine even the most carefully constructed business strategies. Managing Uncertainty: Strategies for Surviving and Thriving in Turbulent Times addresses these new challenges, assessing the sources of business turbulence, how to classify uncertainty, and the different ways in which uncertainty can be embraced to allow greater innovation and growth. Drawing on examples from around the world, the book presents the most recent ideas on what it means to manage uncertainty, from practitioners, academics, and consultants. Addresses the challenges of managing uncertainty in business Presents a step-by-step guide to managing business uncertainty Draws examples from major international companies, including Intel, Procter & Gamble, Siemens, Boeing, Quinetiq, Philips, China Telecom, Ford, Apple, Shell, Glaxo SmithKline and many more Written for business leaders and managers looking for new ways to ensure that their businesses continue to thrive in a world of increasing complexity, Managing Uncertainty presents new and innovative ideas about reducing risk by understanding difficult-to-predict shifts.
This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.
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.
An introductory review of uncertainty formalisms by the volume editors begins the volume. The first main part of the book introduces some of the general problems dealt with in research. The second part is devoted to case studies; each presentation in this category has a well-delineated application problem and an analyzed solution based on an uncertainty formalism. The final part reports on developments of uncertainty formalisms and supporting technology, such as automated reasoning systems, that are vital to making these formalisms applicable. The book ends with a useful subject index. There is considerable synergy between the papers presented. The representative collection of case studies and associated techniques make the volume a particularly coherent and valuable resource. It will be indispensable reading for researchers and professionals interested in the application of uncertainty formalisms as well as for newcomers to the topic.