Elements for a Theory of Decision in Uncertainty
Elements for a Theory of Decision in Uncertainty

Author: Jaime Gil-Aluja

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 344

ISBN-13: 147573011X

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Decisions in uncertainty Scientific reaction to change During latter decades, the basic elements that have traditionally made up the society in wh ich economic activity develops, have been submitted to the effect of multiple aggressions as a consequence of the outcome of events motivated by the nature itself of the human being, always seeking a level of happiness that is never reached. In a very brief manner we are accustomed to mention these by using words such as revolution, profound changes, convulsions . . . . , the results of which are manifest through non-linear reactions that lead to a future charged with uncertainty. To get to know, explain and treat this new world constitutes one of the many objectives of those who desire a society in the service of man, and for those who aspire to the fact of the concept of mutuality transcending the use less limits of the printed word. But for this it will be necessary to overcome a whole realm of obstacles placed in the way by those comfortably embedded in old principles, decrepit ideas and are not willing to open the windows of their mind to receive the fresh air of a new era.

Theory of Decision Under Uncertainty
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.

The Analytics of Uncertainty and Information
The Analytics of Uncertainty and Information

Author: Sushil Bikhchandani

Publisher: Cambridge University Press

Published: 2013-08-12

Total Pages: 509

ISBN-13: 1107433762

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There has been explosive progress in the economic theory of uncertainty and information in the past few decades. This subject is now taught not only in departments of economics but also in professional schools and programs oriented toward business, government and administration, and public policy. This book attempts to unify the subject matter in a simple, accessible manner. Part I of the book focuses on the economics of uncertainty; Part II examines the economics of information. This revised and updated second edition places a greater focus on game theory. New topics include posted-price markets, mechanism design, common-value auctions, and the one-shot deviation principle for repeated games.

Decision Making under Deep Uncertainty
Decision Making under Deep Uncertainty

Author: Vincent A. W. J. Marchau

Publisher: Springer

Published: 2019-04-04

Total Pages: 408

ISBN-13: 3030052524

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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.

Decision Making Under Uncertainty
Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2015-07-24

Total Pages: 350

ISBN-13: 0262331713

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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.

Decision Making Under Uncertainty and Reinforcement Learning
Decision Making Under Uncertainty and Reinforcement Learning

Author: Christos Dimitrakakis

Publisher: Springer Nature

Published: 2022-12-02

Total Pages: 251

ISBN-13: 3031076141

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This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in introductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.

Info-Gap Decision Theory
Info-Gap Decision Theory

Author: Yakov Ben-Haim

Publisher: Elsevier

Published: 2006-10-11

Total Pages: 385

ISBN-13: 0080465706

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Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. Info-Gap Decision Theory is written for decision analysts. The term "decision analyst" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made. This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently "hybrid" models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais "paradoxes", are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models. - New theory developed systematically - Many examples from diverse disciplines - Realistic representation of severe uncertainty - Multi-faceted approach to risk - Quantitative model-based decision theory

Decision Theory
Decision Theory

Author: D.J. White

Publisher: Routledge

Published: 2018-02-06

Total Pages: 291

ISBN-13: 1351523325

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All of human life may be seen as a process of decision-making, but it is only in recent years and in response to the needs of the large and complex organizations characterizing our society that this process has been subjected to scientifi c scrutiny. Out of this scrutiny-undertaken by a wide range of professionals in economics, administration, management, statistics, psychology, engineering, computer science, operations research, and systems analysis-there has begun to emerge a body of theory that has profound implications for improving practical decision-making. This book is the fi rst to bring together all the various aspects of decision theory into one cohesive treatment.

Decision Theory with a Human Face
Decision Theory with a Human Face

Author: Richard Bradley

Publisher: Cambridge University Press

Published: 2017-10-26

Total Pages: 351

ISBN-13: 1107003210

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Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.