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Optimal Decisions Under Uncertainty
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Author : Jatikumar Sengupta
language : en
Publisher: Springer
Release Date : 1981
Optimal Decisions Under Uncertainty written by Jatikumar Sengupta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Decision-making categories.
Optimal Decisions Under Uncertainty
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Author : J. K. Sengupta
language : en
Publisher:
Release Date : 1985-01-01
Optimal Decisions Under Uncertainty written by J. K. Sengupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-01-01 with categories.
Optimal Decisions Under Uncertainty
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Author : J K Sengupta
language : en
Publisher:
Release Date : 1981-09-01
Optimal Decisions Under Uncertainty written by J K Sengupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981-09-01 with categories.
Optimal Decisions Under Uncertainty
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Author : J.K. Sengupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Optimal Decisions Under Uncertainty written by J.K. Sengupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Understanding the stochastic enviornment is as much important to the manager as to the economist. From production and marketing to financial management, a manager has to assess various costs imposed by uncertainty. The economist analyzes the role of incomplete and too often imperfect information structures on the optimal decisions made by a firm. The need for understanding the role of uncertainty in quantitative decision models, both in economics and management science provide the basic motivation of this monograph. The stochastic environment is analyzed here in terms of the following specific models of optimization: linear and quadratic models, linear programming, control theory and dynamic programming. Uncertainty is introduced here through the para meters, the constraints, and the objective function and its impact evaluated. Specifically recent developments in applied research are emphasized, so that they can help the decision-maker arrive at a solution which has some desirable charac teristics like robustness, stability and cautiousness. Mathematical treatment is kept at a fairly elementary level and applied as pects are emphasized much more than theory. Moreover, an attempt is made to in corporate the economic theory of uncertainty into the stochastic theory of opera tions research. Methods of optimal decision rules illustrated he re are applicable in three broad areas: (a) applied economic models in resource allocation and economic planning, (b) operations research models involving portfolio analysis and stochastic linear programming and (c) systems science models in stochastic control and adaptive behavior.
Decision Making Under Uncertainty
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2015-07-24
Decision Making Under Uncertainty written by Mykel J. Kochenderfer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-24 with Computers categories.
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.
Bounded Rationality In Decision Making Under Uncertainty Towards Optimal Granularity
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Author : Joe Lorkowski
language : en
Publisher: Springer
Release Date : 2017-07-01
Bounded Rationality In Decision Making Under Uncertainty Towards Optimal Granularity written by Joe Lorkowski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-01 with Technology & Engineering categories.
This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making. Drawing on the Nobel-prize-winning studies by Kahneman and Tversky, researchers have found many examples of seemingly irrational decisions: e.g., we overestimate the probability of rare events. Our explanation is that since human abilities to process information are limited, we operate not with the exact values of relevant quantities, but with “granules” that contain these values. We show that optimization under such granularity indeed leads to observed human behavior. In particular, for the first time, we explain the mysterious empirical dependence of betting odds on actual probabilities. This book can be recommended to all students interested in human decision-making, to researchers whose work involves human decisions, and to practitioners who design and employ systems involving human decision-making —so that they can better utilize our ability to make decisions under uncertainty.
Irreversible Decisions Under Uncertainty
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Author : Svetlana Boyarchenko
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-26
Irreversible Decisions Under Uncertainty written by Svetlana Boyarchenko and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-26 with Business & Economics categories.
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.
Decision Making Under Uncertainty In Financial Markets
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Author : Jonas Ekblom
language : en
Publisher: Linköping University Electronic Press
Release Date : 2018-09-13
Decision Making Under Uncertainty In Financial Markets written by Jonas Ekblom and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-13 with categories.
This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled. The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.
Managerial Decisions Under Uncertainty
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Author : Bruce F. Baird
language : en
Publisher: John Wiley & Sons
Release Date : 1991-01-16
Managerial Decisions Under Uncertainty written by Bruce F. Baird and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-01-16 with Business & Economics categories.
How to improve decision-making skills in realistic situations and do it in a reasonably nonmathematical fashion. Develops practical techniques for deciding upon the best strategies in a variety of situations. Provides methods for reducing complex problems to easily-drawn decision diagrams (trees), supported by real-world examples. Includes detailed cases that employ the methods described in the text. Each chapter contains illustrative examples and exercises.
Decision Making Under Uncertainty
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2015-07-17
Decision Making Under Uncertainty written by Mykel J. Kochenderfer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-17 with Computers categories.
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.