Theory of the Decision/problem State
Author | : Duncan L. Dieterly |
Publisher | : |
Total Pages | : 26 |
Release | : 1980 |
Genre | : Decision making |
ISBN | : |
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Author | : Duncan L. Dieterly |
Publisher | : |
Total Pages | : 26 |
Release | : 1980 |
Genre | : Decision making |
ISBN | : |
Author | : Duncan L. Dieterly |
Publisher | : |
Total Pages | : 18 |
Release | : 1980 |
Genre | : Decision making |
ISBN | : |
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 | : Duncan L. Dieterly |
Publisher | : |
Total Pages | : 26 |
Release | : 1980 |
Genre | : Decision making |
ISBN | : |
Author | : Martin Peterson |
Publisher | : Cambridge University Press |
Total Pages | : 351 |
Release | : 2017-03-30 |
Genre | : Business & Economics |
ISBN | : 1107151597 |
A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.
Author | : Richard Bradley |
Publisher | : Cambridge University Press |
Total Pages | : 351 |
Release | : 2017-10-26 |
Genre | : Business & Economics |
ISBN | : 1107003210 |
Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.
Author | : Patrice Perny |
Publisher | : Springer |
Total Pages | : 442 |
Release | : 2013-10-08 |
Genre | : Computers |
ISBN | : 9783642415746 |
This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Algorithmic Decision Theory, ADT 2013, held in November 2013 in Bruxelles, Belgium. The 33 revised full papers presented were carefully selected from more than 70 submissions, covering preferences in reasoning and decision making, uncertainty and robustness in decision making, multi-criteria decision analysis and optimization, collective decision making, learning and knowledge extraction for decision support.
Author | : José Luis Bermúdez |
Publisher | : OUP Oxford |
Total Pages | : 199 |
Release | : 2009-02-19 |
Genre | : Philosophy |
ISBN | : 0191567728 |
The concept of rationality is a common thread through the human and social sciences — from political science to philosophy, from economics to sociology, and from management science to decision analysis. But what counts as rational action and rational behavior? José Luis Bermúdez explores decision theory as a theory of rationality. Decision theory is the mathematical theory of choice and for many social scientists it makes the concept of rationality mathematically tractable and scientifically legitimate. Yet rationality is a concept with several dimensions and the theory of rationality has different roles to play. It plays an action-guiding role (prescribing what counts as a rational solution of a given decision problem). It plays a normative role (giving us the tools to pass judgment not just on how a decision problem was solved, but also on how it was set up in the first place). And it plays a predictive/explanatory role (telling us how rational agents will behave, or why they did what they did). This controversial but accessible book shows that decision theory cannot play all of these roles simultaneously. And yet, it argues, no theory of rationality can play one role without playing the other two. The conclusion is that there is no hope of taking decision theory as a theory of rationality.
Author | : Thomas S. Ferguson |
Publisher | : Academic Press |
Total Pages | : 409 |
Release | : 2014-07-10 |
Genre | : Mathematics |
ISBN | : 1483221237 |
Mathematical Statistics: A Decision Theoretic Approach presents an investigation of the extent to which problems of mathematical statistics may be treated by decision theory approach. This book deals with statistical theory that could be justified from a decision-theoretic viewpoint. Organized into seven chapters, this book begins with an overview of the elements of decision theory that are similar to those of the theory of games. This text then examines the main theorems of decision theory that involve two more notions, namely the admissibility of a decision rule and the completeness of a class of decision rules. Other chapters consider the development of theorems in decision theory that are valid in general situations. This book discusses as well the invariance principle that involves groups of transformations over the three spaces around which decision theory is built. The final chapter deals with sequential decision problems. This book is a valuable resource for first-year graduate students in mathematics.
Author | : Michael Zabarankin |
Publisher | : Springer Science & Business Media |
Total Pages | : 254 |
Release | : 2013-12-16 |
Genre | : Business & Economics |
ISBN | : 1461484715 |
Statistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more. The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.