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Author : Martin Peterson
Publisher : Cambridge University Press
Page : 351 pages
File Size : 42,6 Mb
Release : 2017-03-30
Category : Business & Economics
ISBN : 9781107151598
A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.
Author : Martin Peterson
Publisher : Cambridge University Press
Page : 351 pages
File Size : 47,9 Mb
Release : 2017-03-30
Category : Science
ISBN : 9781108179522
Now revised and updated, this introduction to decision theory is both accessible and comprehensive, covering topics including decision making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, with all concepts and results explained in non-technical and intuitive as well as more formal ways. There are now over 140 exercises with solutions, along with a glossary of key terms and concepts. This second edition includes a new chapter on risk aversion as well as updated discussions of numerous central ideas, including Newcomb's problem, prisoner's dilemmas, and Arrow's impossibility theorem. The book will appeal particularly to philosophy students but also to readers in a range of disciplines, from computer science and psychology to economics and political science.
Author : Michael D. Resnik
Publisher : U of Minnesota Press
Page : 244 pages
File Size : 40,6 Mb
Release : 1987
Category : Decision making
ISBN : 1452902283
Author : Mykel J. Kochenderfer
Publisher : MIT Press
Page : 350 pages
File Size : 46,6 Mb
Release : 2015-07-24
Category : Computers
ISBN : 9780262331715
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 : Steve Tadelis
Publisher : Princeton University Press
Page : 416 pages
File Size : 54,6 Mb
Release : 2013-01-06
Category : Business & Economics
ISBN : 9780691129082
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 : John Pratt,Howard Raiffa,Robert Schlaifer
Publisher : MIT Press
Page : 0 pages
File Size : 43,9 Mb
Release : 2008-01-25
Category : Business & Economics
ISBN : 9780262662062
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 : Anthony Kelly
Publisher : Cambridge University Press
Page : 228 pages
File Size : 42,7 Mb
Release : 2003-03-27
Category : Business & Economics
ISBN : 1139438131
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 : Silvia Bacci,Bruno Chiandotto
Publisher : CRC Press
Page : 305 pages
File Size : 48,7 Mb
Release : 2019-07-11
Category : Mathematics
ISBN : 9781351621397
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty Illustrates expected utility theory and its extensions Describes approaches to elicit the utility function Reviews classical and Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory
Author : Herman Chernoff,Lincoln E. Moses
Publisher : Courier Corporation
Page : 386 pages
File Size : 47,9 Mb
Release : 1986-01-01
Category : Mathematics
ISBN : 0486652181
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 : John Bather
Publisher : John Wiley & Sons
Page : 216 pages
File Size : 50,9 Mb
Release : 2000-07-26
Category : Education
ISBN : UOM:39015042404502
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 : Richard Bradley
Publisher : Cambridge University Press
Page : 351 pages
File Size : 50,8 Mb
Release : 2017-10-26
Category : Mathematics
ISBN : 9781107003217
Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.
Author : Michael D. Resnik
Publisher : Unknown
Page : 221 pages
File Size : 46,5 Mb
Release : 1987
Category : Mathematics
ISBN : 0816614393
Author : Itzhak Gilboa
Publisher : Cambridge University Press
Page : 216 pages
File Size : 50,8 Mb
Release : 2009-03-16
Category : Business & Economics
ISBN : 9780521517324
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.
Author : Anatol Rapoport
Publisher : Springer Science & Business Media
Page : 439 pages
File Size : 48,6 Mb
Release : 2013-03-14
Category : Business & Economics
ISBN : 9789401578400
This book presents the content of a year's course in decision processes for third and fourth year students given at the University of Toronto. A principal theme of the book is the relationship between normative and descriptive decision theory. The distinction between the two approaches is not clear to everyone, yet it is of great importance. Normative decision theory addresses itself to the question of how people ought to make decisions in various types of situations, if they wish to be regarded (or to regard themselves) as 'rational'. Descriptive decision theory purports to describe how people actually make decisions in a variety of situations. Normative decision theory is much more formalized than descriptive theory. Especially in its advanced branches, normative theory makes use of mathematicallanguage, mode of discourse, and concepts. For this reason, the definitions of terms encountered in normative decision theory are precise, and its deductions are rigorous. Like the terms and assertions of other branches of mathematics, those of mathematically formalized decision theory need not refer to anything in the 'real', i. e. the observable, world. The terms and assertions can be interpreted in the context of models of real li fe situations, but the verisimilitude of the models is not important. They are meant to capture only the essentials of adecision situation, which in reallife may be obscured by complex details and ambiguities. It is these details and ambiguities, however, that may be crucial in determining the outcomes of the decisions.
Author : Simon D. Parsons,Piotr Gymtrasiewicz,Michael Wooldridge
Publisher : Springer Science & Business Media
Page : 403 pages
File Size : 54,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461511076
Game Theory And Decision Theory In Agent-Based Systems is a collection of papers from international leading researchers, that offers a broad view of the many ways game theory and decision theory can be applied in agent-based systems, from standard applications of the core elements of the theory to more cutting edge developments. The range of topics discussed in this book provide the reader with the first comprehensive volume that reflects both the depth and breadth of work in applying techniques from game theory and decision theory to design agent-based systems. Chapters include: Selecting Partners; Evolution of Agents with Moral Sentiments in an IPD Exercise; Dynamic Desires; Emotions and Personality; Decision-Theoretic Approach to Game Theory; Shopbot Economics; Finding the Best Way to Join in; Shopbots and Pricebots in Electronic Service Markets; Polynomial Time Mechanisms; Multi-Agent Q-learning and Regression Trees; Satisficing Equilibria; Investigating Commitment Flexibility in Multi-agent Contracts; Pricing in Agent Economies using Multi-agent Q-learning; Using Hypergames to Increase Planned Payoff and Reduce Risk; Bilateral Negotiation with Incomplete and Uncertain Information; Robust Combinatorial Auction Protocol against False-name Bids.