Uncertainty In Strategic Decision Making
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| Author | : Vincent A. W. J. Marchau |
| Publisher | : Springer |
| Total Pages | : 408 |
| Release | : 2019-04-04 |
| Genre | : Business & Economics |
| ISBN | : 3030052524 |
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.
| Author | : Kristian J. Sund |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 242 |
| Release | : 2016-11-14 |
| Genre | : Business & Economics |
| ISBN | : 1786351692 |
In this book, leading researchers on Managerial and Organizational Cognition consider the foundations of individual and social cognition and their effect on strategic decision-making.
| Author | : Richard Friberg |
| Publisher | : MIT Press |
| Total Pages | : 395 |
| Release | : 2015-11-13 |
| Genre | : Business & Economics |
| ISBN | : 0262528193 |
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.
| Author | : Panagiotis E. Petrakis |
| Publisher | : Springer |
| Total Pages | : 220 |
| Release | : 2016-04-29 |
| Genre | : Business & Economics |
| ISBN | : 1137460792 |
Uncertainty in Entrepreneurial Decision Making fills an existing gap in understanding three key concepts of business management: entrepreneurship, uncertainty, and strategy. By extending the impact of uncertainty on entrepreneurship and the role of strategy in reducing uncertainty, Petrakis and Konstantakopoulou emphasize that uncertainty can be converted into creative advantage. Given that the business environment is changing both very quickly and very often, any wrong decisions taken can lead to devastation. This exciting new volume explains the reasons why we cannot see the complete the future and our position in it. This uncertainty affects entrepreneurship and how it can be turned into a competitive advantage for businesses sustainability.
| Author | : Mykel J. Kochenderfer |
| Publisher | : MIT Press |
| Total Pages | : 350 |
| Release | : 2015-07-24 |
| Genre | : Computers |
| ISBN | : 0262331713 |
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.
| Author | : Meir Finkel |
| Publisher | : Stanford University Press |
| Total Pages | : 337 |
| Release | : 2011-02-28 |
| Genre | : Political Science |
| ISBN | : 0804774897 |
On Flexibility presents a force planning concept that will enable armies to cope with the growing diversity of battlefield requirements, and especially with technological and doctrinal surprises, through applied adaptability and flexibility, minimizing the over dependence on intelligence and prediction involved in this process today.
| Author | : Richard J. Arend |
| Publisher | : Springer Nature |
| Total Pages | : 466 |
| Release | : 2024-01-03 |
| Genre | : Business & Economics |
| ISBN | : 303148553X |
Knight (1921) defines uncertainty as an informational market failure that, while being detrimental to most existing businesses, presents possible profitable opportunities for others. This book builds upon that classic work by providing an analysis of the alternative approaches to strategic decision-making under such uncertainty. It covers what uncertainty is, why it is important, and what connections it has to business and related fields, culminating in a new and comprehensive typology and a valuable guide for how to appropriately address various types of uncertainties, even under AI. It clarifies the current terminological and categorical confusion about ‘unknowns’ while complementing the mathematical, probability-based approaches that treat uncertainty as ‘knowable’ (i.e., as risk). It corrects the mistaken approaches that treat ‘unknowables’ as ‘shapeable’ or ‘discoverable’. This book widens the perspective for viewing uncertainty, in terms of its impacts across humanity, by offering a shrewder understanding of what roles uncertainties play in human activity. It will appeal to academics across business, economics, philosophy, and other disciplines looking for approaches to apply, test, and hone for dealing with decision-making under uncertainty.
| Author | : Institute of Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 280 |
| Release | : 2013-05-20 |
| Genre | : Political Science |
| ISBN | : 0309290236 |
The U.S. Environmental Protection Agency (EPA) is one of several federal agencies responsible for protecting Americans against significant risks to human health and the environment. As part of that mission, EPA estimates the nature, magnitude, and likelihood of risks to human health and the environment; identifies the potential regulatory actions that will mitigate those risks and protect public health1 and the environment; and uses that information to decide on appropriate regulatory action. Uncertainties, both qualitative and quantitative, in the data and analyses on which these decisions are based enter into the process at each step. As a result, the informed identification and use of the uncertainties inherent in the process is an essential feature of environmental decision making. EPA requested that the Institute of Medicine (IOM) convene a committee to provide guidance to its decision makers and their partners in states and localities on approaches to managing risk in different contexts when uncertainty is present. It also sought guidance on how information on uncertainty should be presented to help risk managers make sound decisions and to increase transparency in its communications with the public about those decisions. Given that its charge is not limited to human health risk assessment and includes broad questions about managing risks and decision making, in this report the committee examines the analysis of uncertainty in those other areas in addition to human health risks. Environmental Decisions in the Face of Uncertainty explains the statement of task and summarizes the findings of the committee.
| Author | : Paul Schoemaker |
| Publisher | : Simon and Schuster |
| Total Pages | : 329 |
| Release | : 2012-12-25 |
| Genre | : Business & Economics |
| ISBN | : 0743234189 |
What manager is not anxious about the future? We live in a white-knuckled age of rapid technological change and global instability. But uncertainty is not the enemy, says management expert Paul J. H. Schoemaker. It is where the greatest opportunities are. To unlock these opportunities, however, requires a very different approach to strategy and implementation. In this pioneering book, Dr. Schoemaker presents a systematic approach that combines concepts such as scenario planning, options thinking, and dynamic monitoring to create novel strategies for profiting from ambiguity. Building on his experience with more than one hundred consulting projects in fields ranging from health care to manufacturing, from utilities to financial services, Schoemaker shows how major corporations throughout the world have used his pathbreaking methodology to prepare for an uncertain future and profit from it. In this first comprehensive approach to the subject, Schoemaker shows the reader (1) how to develop and analyze multiple industry scenarios, (2) craft nimble strategies with just the right amount of flexibility, (3) implement them using an options approach, and (4) make real-time adjustments through dynamic monitoring. As a leading academic thinker and practitioner, the author draws on the frontiers of decision science, organization theory, strategy, and cognitive psychology to integrate the most practical contributions these various fields have made to navigating uncertainty. More than any other capability, skill in seizing initiatives in shifting, unpredictable circumstances is the key to success. Profiting from Uncertainty provides a road map to do just that. This book was first published in 2002, well ahead of the mega turmoil that befell the world in 2008 and beyond. The methods and tools described here have been used by many companies and are even more relevant today than when originally published. You can’t do without them.
| Author | : Itzhak Gilboa |
| Publisher | : Cambridge University Press |
| Total Pages | : 216 |
| Release | : 2009-03-16 |
| Genre | : Business & Economics |
| ISBN | : 052151732X |
This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.
