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Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2015-07-24

Total Pages: 350

ISBN-13: 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.

Public Policy in an Uncertain World

Author: Charles F. Manski

Publisher: Harvard University Press

Published: 2013-02-14

Total Pages: 218

ISBN-13: 0674067541

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Manski argues that public policy is based on untrustworthy analysis. Failing to account for uncertainty in an uncertain world, policy analysis routinely misleads policy makers with expressions of certitude. Manski critiques the status quo and offers an innovation to improve both how policy research is conducted and how it is used by policy makers.

Completing the Forecast

Author: National Research Council

Publisher: National Academies Press

Published: 2006-10-09

Total Pages: 124

ISBN-13: 0309180538

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Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.

Theory of Decision Under Uncertainty

Author: Itzhak Gilboa

Publisher: Cambridge University Press

Published: 2009-03-16

Total Pages: 216

ISBN-13: 052151732X

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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.

Rational Choice in an Uncertain World

Author: Reid Hastie

Publisher: SAGE

Published: 2010

Total Pages: 393

ISBN-13: 1412959039

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In the Second Edition of Rational Choice in an Uncertain World the authors compare the basic principles of rationality with actual behaviour in making decisions. They describe theories and research findings from the field of judgment and decision making in a non-technical manner, using anecdotes as a teaching device. Intended as an introductory textbook for advanced undergraduate and graduate students, the material not only is of scholarly interest but is practical as well. The Second Edition includes: - more coverage on the role of emotions, happiness, and general well-being in decisions - a summary of the new research on the neuroscience of decision processes - more discussion of the adaptive value of (non-rational heuristics) - expansion of the graphics for decision trees, probability trees, and Venn diagrams.

Decision Making Under Uncertainty in Electricity Markets

Author: Antonio J. Conejo

Publisher: Springer Science & Business Media

Published: 2010-09-08

Total Pages: 549

ISBN-13: 1441974210

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Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.

Radical Uncertainty: Decision-Making Beyond the Numbers

Author: John Kay

Publisher: W. W. Norton & Company

Published: 2020-03-17

Total Pages: 407

ISBN-13: 1324004789

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Much economic advice is bogus quantification, warn two leading experts in this essential book, now with a preface on COVID-19. Invented numbers offer a false sense of security; we need instead robust narratives that give us the confidence to manage uncertainty. “An elegant and careful guide to thinking about personal and social economics, especially in a time of uncertainty. The timing is impeccable." — Christine Kenneally, New York Times Book Review Some uncertainties are resolvable. The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible. Before President Barack Obama made the fateful decision to send in the Navy Seals, his advisers offered him wildly divergent estimates of the odds that Osama bin Laden would be in the Abbottabad compound. In 2000, no one—not least Steve Jobs—knew what a smartphone was; how could anyone have predicted how many would be sold in 2020? And financial advisers who confidently provide the information required in the standard retirement planning package—what will interest rates, the cost of living, and your state of health be in 2050?—demonstrate only that their advice is worthless. The limits of certainty demonstrate the power of human judgment over artificial intelligence. In most critical decisions there can be no forecasts or probability distributions on which we might sensibly rely. Instead of inventing numbers to fill the gaps in our knowledge, we should adopt business, political, and personal strategies that will be robust to alternative futures and resilient to unpredictable events. Within the security of such a robust and resilient reference narrative, uncertainty can be embraced, because it is the source of creativity, excitement, and profit.

Uncertain Decisions

Author: Luigi Luini

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 360

ISBN-13: 1461550831

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Uncertain Decisions: Bridging Theory and Experiments presents advanced directions of thinking on decision theory - in particular the more recent contributions on non-expected utility theory, fuzzy decision theory and case-based theory. This work also provides theoretical insights on measures of risk aversion and on new problems for general equilibrium analysis. It analyzes how the thinking that underlies the theories described above spills over into real decisions, and how the thinking that underlies these real decisions can explain the discrepancies between theoretical approaches and actual behavior. This work elaborates on how the most recent laboratory experiments have become an important source both for evaluating the leading theory of choice and decision, and for contributing to the formation of new models regarding the subject.

Theory and Approaches of Group Decision Making with Uncertain Linguistic Expressions

Author: Hai Wang

Publisher: Springer

Published: 2019-01-12

Total Pages: 222

ISBN-13: 9811337357

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This book mainly introduces a series of theory and approaches of group decision-making based on several types of uncertain linguistic expressions and addresses their applications. The book pursues three major objectives: (1) to introduce some techniques to model several types of natural linguistic expressions; (2) to handle these expressions in group decision-making; and (3) to clarify the involved approaches by practical applications. The book is especially valuable for readers to understand how linguistic expressions could be employed and operated to make decisions, and motivates researchers to consider more types of natural linguistic expressions in decision analysis under uncertainties.

Decision Making under Uncertainty

Author: Kerstin Preuschoff

Publisher: Frontiers Media SA

Published: 2015-06-16

Total Pages: 144

ISBN-13: 2889194663

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Most decisions in life are based on incomplete information and have uncertain consequences. To successfully cope with real-life situations, the nervous system has to estimate, represent and eventually resolve uncertainty at various levels. A common tradeoff in such decisions involves those between the magnitude of the expected rewards and the uncertainty of obtaining the rewards. For instance, a decision maker may choose to forgo the high expected rewards of investing in the stock market and settle instead for the lower expected reward and much less uncertainty of a savings account. Little is known about how different forms of uncertainty, such as risk or ambiguity, are processed and learned about and how they are integrated with expected rewards and individual preferences throughout the decision making process. With this Research Topic we aim to provide a deeper and more detailed understanding of the processes behind decision making under uncertainty.