Managing In Uncertainty Theory And Practice

This book provides a new point of view on the subject of the management of uncertainty. It covers a wide variety of both theoretical and practical issues involving the analysis and management of uncertainty in the fields of finance, management and marketing. Audience: Researchers and professionals from operations research, management science and economics.

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.

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment in any type of industry. It is concerned with the quantification of uncertainties in the presence of data, model(s) and knowledge about the system, and offers a technical contribution to decision-making processes whilst acknowledging industrial constraints. The approach presented can be applied to a range of different business contexts, from research or early design through to certification or in-service processes. The authors aim to foster optimal trade-offs between literature-referenced methodologies and the simplified approaches often inevitable in practice, owing to data, time or budget limitations of technical decision-makers. Uncertainty in Industrial Practice: Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework. Presents methods for organizing and treating uncertainties in a generic and prioritized perspective. Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints. Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods. Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries. This book provides a guide to dealing with quantitative uncertainty in engineering and modelling and is aimed at practitioners, including risk-industry regulators and academics wishing to develop industry-realistic methodologies.

A comprehensive framework for assessing strategies for managing risk and uncertainty, integrating theory and practice and synthesizing insights from many fields. This book offers a framework for making decisions under risk and uncertainty. Synthesizing research from economics, finance, decision theory, management, and other fields, the book provides a set of tools and a way of thinking that determines the relative merits of different strategies. It takes as its premise that we make better decisions if we use the whole toolkit of economics and related fields to inform our decision making. The text explores the distinction between risk and uncertainty and covers standard models of decision making under risk as well as more recent work on decision making under uncertainty, with a particular focus on strategic interaction. It also examines the implications of incomplete markets for managing under uncertainty. It presents four core strategies: a benchmark strategy (proceeding as if risk and uncertainty were low), a financial hedging strategy (valuable if there is much risk), an operational hedging strategy (valuable for conditions of much uncertainty), and a flexible strategy (valuable if there is much risk and/or uncertainty). The book then examines various aspects of these strategies in greater depth, building on empirical work in several different fields. Topics include price-setting, real options and Monte Carlo techniques, organizational structure, and behavioral biases. Many chapters include exercises and appendixes with additional material. The book can be used in graduate or advanced undergraduate courses in risk management, as a guide for researchers, or as a reference for management practitioners.

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.

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

In this book, Michael W. Kramer applies uncertainty reduction theory (URT)--a key theory in current communication scholarship--to the context of organizational communication. Examining URT and the range of research applicable to organizational settings, Kramer proposes a groundbreaking theory of managing uncertainty (TMU), which synthesizes prior research while also addressing its criticisms. Examples are provided to illustrate the principles of the TMU at both the individual and collective (group/organizational) levels of analysis. Original studies based on the theory show that it provides a useful extension of URT, addressing some concerns raised by critics of that earlier model. Kramer illustrates that, as a model in progress, TMU will change as new research and insights build upon it. Managing Uncertainty in Organizational Communication assists readers in understanding and researching uncertainty in communication, which encourages additional changes and improvements to the model. It is of primary interest to scholars, researchers, and practitioners in organizational, interpersonal, and group communication.

This book studies the variety of organizational strategies selected to cope with critical uncertainties during crises. This research formulates and applies an institutional sense-making model to explain the selection of strategies for coping with uncertainties during crises to answer the question why some organizations select a rule-based strategy to cope with uncertainties, whereas others pursue a more ad hoc-based strategy. It finds that the level of institutionalization does not affect strategy selection in the initial phase of responding to crises; that three rigidity effects can be identified in the selection of sense-making strategies once organizations have faced the failure of their selected strategies; that discontinuities in the feedback loop of sense-making do not necessarily move organizations to switch their sense-making strategies, but interact with institutionalization to contribute to switching sense-making strategies. This book bridges the gap between institutional thinking and crisis management theorizing. A major step forward in the world of crisis management studies! ——Professor Arjen Boin, Leiden University, the Netherlands In a world of increasingly complex, sociotechnical systems interacting in high-risk environments, Professor Lu’s analysis of how organizations manage uncertainty is both timely and profound. ——Professor Louise K. Comfort, Director, Center for Disaster Management, University of Pittsburgh, USA Prof. Lu greatly enhances our understanding of how organizations cope with uncertainty and make sense of their challenges under the pressures of catastrophe. ——Dr. Arnold M. Howitt, Faculty Co-Director, Program on Crisis Leadership, Harvard Kennedy School, USA This book provides not only a theory of crisis management but also a key concept around which research and practice can be conducted. ——Professor Naim Kapucu, Director of School of Public Administration, University of Central Florida, USA A generic institutional model for analyzing and managing hazards, disasters and crises worldwide. ——Professor Joop Koppenjan, Erasmus University Rotterdam, the Netherlands This book has done an excellent job in opening the black box of how organizations make sense of the crisis situations they face and develop strategies to respond. It should be read by all of us who wish for a peaceful and safe world. ——Professor Lan Xue, Dean of School of Public Policy and Management, Tsinghua University, China