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| 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 | : Herman Chernoff |
| Publisher | : Courier Corporation |
| Total Pages | : 386 |
| Release | : 1986-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780486652184 |
This well-respected introduction to statistics and statistical theory covers data processing, probability and random variables, utility and descriptive statistics, computation of Bayes strategies, models, testing hypotheses, and much more. 1959 edition.
| 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 | : John Pratt |
| Publisher | : MIT Press |
| Total Pages | : 0 |
| Release | : 2008-01-25 |
| Genre | : Business & Economics |
| ISBN | : 026266206X |
The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems. Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.
| Author | : Steve Tadelis |
| Publisher | : Princeton University Press |
| Total Pages | : 416 |
| Release | : 2013-01-06 |
| Genre | : Business & Economics |
| ISBN | : 0691129088 |
The definitive introduction to game theory This comprehensive textbook introduces readers to the principal ideas and applications of game theory, in a style that combines rigor with accessibility. Steven Tadelis begins with a concise description of rational decision making, and goes on to discuss strategic and extensive form games with complete information, Bayesian games, and extensive form games with imperfect information. He covers a host of topics, including multistage and repeated games, bargaining theory, auctions, rent-seeking games, mechanism design, signaling games, reputation building, and information transmission games. Unlike other books on game theory, this one begins with the idea of rationality and explores its implications for multiperson decision problems through concepts like dominated strategies and rationalizability. Only then does it present the subject of Nash equilibrium and its derivatives. Game Theory is the ideal textbook for advanced undergraduate and beginning graduate students. Throughout, concepts and methods are explained using real-world examples backed by precise analytic material. The book features many important applications to economics and political science, as well as numerous exercises that focus on how to formalize informal situations and then analyze them. Introduces the core ideas and applications of game theory Covers static and dynamic games, with complete and incomplete information Features a variety of examples, applications, and exercises Topics include repeated games, bargaining, auctions, signaling, reputation, and information transmission Ideal for advanced undergraduate and beginning graduate students Complete solutions available to teachers and selected solutions available to students
| Author | : Anthony Kelly |
| Publisher | : Cambridge University Press |
| Total Pages | : 228 |
| Release | : 2003-03-27 |
| Genre | : Business & Economics |
| ISBN | : 9781139438131 |
Game theory is a key element in most decision-making processes involving two or more people or organisations. This book explains how game theory can predict the outcome of complex decision-making processes, and how it can help you to improve your own negotiation and decision-making skills. It is grounded in well-established theory, yet the wide-ranging international examples used to illustrate its application offer a fresh approach to an essential weapon in the armoury of the informed manager. The book is accessibly written, explaining in simple terms the underlying mathematics behind games of skill, before moving on to more sophisticated topics such as zero-sum games, mixed-motive games, and multi-person games, coalitions and power. Clear examples and helpful diagrams are used throughout, and the mathematics is kept to a minimum. It is written for managers, students and decision makers in any field.
| Author | : John Bather |
| Publisher | : John Wiley & Sons |
| Total Pages | : 216 |
| Release | : 2000-07-26 |
| Genre | : Education |
| ISBN | : |
Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author's fluent style will leave the reader with an avid interest in the subject. * Tailored to the needs of students of optimization and decision theory * Written in a lucid style with numerous examples and applications * Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity * Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics * Coverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters * Contains exercises at the end of each chapter, with hints in an appendix Aimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.
| Author | : Michael D. Resnik |
| Publisher | : U of Minnesota Press |
| Total Pages | : 244 |
| Release | : 1987 |
| Genre | : Decision making |
| ISBN | : 9781452902289 |
| Author | : Michael D. Resnik |
| Publisher | : |
| Total Pages | : 221 |
| Release | : 1987 |
| Genre | : Mathematics |
| ISBN | : 9780816614394 |
| 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.
