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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.
Book Synopsis Theory of Decision Under Uncertainty by : Itzhak Gilboa
Download or read book Theory of Decision Under Uncertainty written by Itzhak Gilboa and published by Cambridge University Press. This book was released on 2009-03-16 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
This book provides tools for making decisions in an environment of uncertainty. In Chapter 1 the author explains the most important aspects of the concept of relation. From this start arise the other three concepts that cover practically all processes from which decisions stem. These three concepts are: attribution from which the concept of assignment arises; and grouping, which includes the concept of an original function. The techniques presented, as well as the models and algorithms developed, constitute an invaluable aid for those who must make decisions. Audience: Researchers and graduate students interested in mathematics applied to economics and management.
Book Synopsis Elements for a Theory of Decision in Uncertainty by : Jaime Gil-Aluja
Download or read book Elements for a Theory of Decision in Uncertainty written by Jaime Gil-Aluja and published by Springer Science & Business Media. This book was released on 1999-11-30 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides tools for making decisions in an environment of uncertainty. In Chapter 1 the author explains the most important aspects of the concept of relation. From this start arise the other three concepts that cover practically all processes from which decisions stem. These three concepts are: attribution from which the concept of assignment arises; and grouping, which includes the concept of an original function. The techniques presented, as well as the models and algorithms developed, constitute an invaluable aid for those who must make decisions. Audience: Researchers and graduate students interested in mathematics applied to economics and management.
Whether we like it or not we all feel that the world is uncertain. From choosing a new technology to selecting a job, we rarely know in advance what outcome will result from our decisions. Unfortunately, the standard theory of choice under uncertainty developed in the early forties and fifties turns out to be too rigid to take many tricky issues of choice under uncertainty into account. The good news is that we have now moved away from the early descriptively inadequate modeling of behavior. This book brings the reader into contact with the accomplished progress in individual decision making through the most recent contributions to uncertainty modeling and behavioral decision making. It also introduces the reader into the many subtle issues to be resolved for rational choice under uncertainty.
Book Synopsis Advances in Decision Making Under Risk and Uncertainty by : Mohammed Abdellaoui
Download or read book Advances in Decision Making Under Risk and Uncertainty written by Mohammed Abdellaoui and published by Springer Science & Business Media. This book was released on 2008-08-29 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether we like it or not we all feel that the world is uncertain. From choosing a new technology to selecting a job, we rarely know in advance what outcome will result from our decisions. Unfortunately, the standard theory of choice under uncertainty developed in the early forties and fifties turns out to be too rigid to take many tricky issues of choice under uncertainty into account. The good news is that we have now moved away from the early descriptively inadequate modeling of behavior. This book brings the reader into contact with the accomplished progress in individual decision making through the most recent contributions to uncertainty modeling and behavioral decision making. It also introduces the reader into the many subtle issues to be resolved for rational choice under uncertainty.
This book systematically develops essential concepts in the economics of uncertainty and game theory. It also presents new ideas for further research. The first part deals with the economics of uncertainty, including a discussion of expected utility theory and non-expected utility theories, insurance market, portfolio analysis, principal-agent theory, as well as ethical issues presented in the context of choice under uncertainty. The second part develops an understanding of game theory as a tool for analysing the interactive decision-making process.
Book Synopsis Decision-making Under Uncertainty by : Tapan Biswas
Download or read book Decision-making Under Uncertainty written by Tapan Biswas and published by Palgrave Macmillan. This book was released on 1997 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically develops essential concepts in the economics of uncertainty and game theory. It also presents new ideas for further research. The first part deals with the economics of uncertainty, including a discussion of expected utility theory and non-expected utility theories, insurance market, portfolio analysis, principal-agent theory, as well as ethical issues presented in the context of choice under uncertainty. The second part develops an understanding of game theory as a tool for analysing the interactive decision-making process.
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
Book Synopsis Info-Gap Decision Theory by : Yakov Ben-Haim
Download or read book Info-Gap Decision Theory written by Yakov Ben-Haim and published by Elsevier. This book was released on 2006-10-11 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
As desired, the infonnation demand correspondence is single valued at equilibrium prices. Hence no planner is needed to assign infonnation allocations to individuals. Proposition 4. For any given infonnation price system p E . P (F *), almost every a E A demands a unique combined infonnation structure (although traders may be indifferent among partial infonnation sales from different information allocations, etc. ). In particular, the aggregate excess demand correspondence for net combined infonnation trades is a continuous function. Proof Uniqueness fails only if an agent can obtain the same expected utility from two or more net combined infonnation allocations. If this happens, appropriate slight perturbations of personal probability vectors destroy the equality unless the utility functions and wealth allocations were independent across states. Yet, when utilities and wealths don't depend on states in S, no infonnation to distinguish the states is desired, so that the demand for such infonnation structures must equal zero. To show the second claim, recall that if the correspondence is single valued for almost every agent, then its integral is also single valued. Finally, note that an upper hemicontinuous (by Proposition 2) correspondence which is single valued everywhere is, in fact, a continuous function. [] REFERENCES Allen, Beth (1986a). "The Demand for (Differentiated) Infonnation"; Review of Economic Studies. 53. (311-323). Allen, Beth (1986b). "General Equilibrium with Infonnation Sales"; Theory and Decision. 21. (1-33). Allen, Beth (1990). "Infonnation as an Economic Commodity"; American Economic Review. 80. (268-273).
Book Synopsis Decision Making Under Risk and Uncertainty by : J. Geweke
Download or read book Decision Making Under Risk and Uncertainty written by J. Geweke and published by Springer Science & Business Media. This book was released on 1992-08-31 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: As desired, the infonnation demand correspondence is single valued at equilibrium prices. Hence no planner is needed to assign infonnation allocations to individuals. Proposition 4. For any given infonnation price system p E . P (F *), almost every a E A demands a unique combined infonnation structure (although traders may be indifferent among partial infonnation sales from different information allocations, etc. ). In particular, the aggregate excess demand correspondence for net combined infonnation trades is a continuous function. Proof Uniqueness fails only if an agent can obtain the same expected utility from two or more net combined infonnation allocations. If this happens, appropriate slight perturbations of personal probability vectors destroy the equality unless the utility functions and wealth allocations were independent across states. Yet, when utilities and wealths don't depend on states in S, no infonnation to distinguish the states is desired, so that the demand for such infonnation structures must equal zero. To show the second claim, recall that if the correspondence is single valued for almost every agent, then its integral is also single valued. Finally, note that an upper hemicontinuous (by Proposition 2) correspondence which is single valued everywhere is, in fact, a continuous function. [] REFERENCES Allen, Beth (1986a). "The Demand for (Differentiated) Infonnation"; Review of Economic Studies. 53. (311-323). Allen, Beth (1986b). "General Equilibrium with Infonnation Sales"; Theory and Decision. 21. (1-33). Allen, Beth (1990). "Infonnation as an Economic Commodity"; American Economic Review. 80. (268-273).
Book Synopsis Decisions Under Uncertainty by : Ian Jordaan
Download or read book Decisions Under Uncertainty written by Ian Jordaan and published by Cambridge University Press. This book was released on 2005-04-07 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
In this book, Adrian Vermeule shows that any approach to legal interpretation rests on institutional and empirical premises about the capacities of judges and the systemic effects of their rulings. He argues that legal interpretation is above all an exercise in decisionmaking under severe empirical uncertainty.
Book Synopsis Judging Under Uncertainty by : Adrian Vermeule
Download or read book Judging Under Uncertainty written by Adrian Vermeule and published by Harvard University Press. This book was released on 2006 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Adrian Vermeule shows that any approach to legal interpretation rests on institutional and empirical premises about the capacities of judges and the systemic effects of their rulings. He argues that legal interpretation is above all an exercise in decisionmaking under severe empirical uncertainty.
Here, two highly experienced authors present an alternative approach to optimal stopping problems. The basic ideas and techniques of the approach can be explained much simpler than the standard methods in the literature on optimal stopping problems. The monograph will teach the reader to apply the technique to many problems in economics and finance, including new ones. From the technical point of view, the method can be characterized as option pricing via the Wiener-Hopf factorization.
Book Synopsis Irreversible Decisions under Uncertainty by : Svetlana Boyarchenko
Download or read book Irreversible Decisions under Uncertainty written by Svetlana Boyarchenko and published by Springer Science & Business Media. This book was released on 2007-08-26 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here, two highly experienced authors present an alternative approach to optimal stopping problems. The basic ideas and techniques of the approach can be explained much simpler than the standard methods in the literature on optimal stopping problems. The monograph will teach the reader to apply the technique to many problems in economics and finance, including new ones. From the technical point of view, the method can be characterized as option pricing via the Wiener-Hopf factorization.
