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Decision Systems and Nonstochastic Randomness

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Synopses & Reviews

Publisher Comments:

"Decision Systems and Nonstochastic Randomness" presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models. Each self-contained chapter of this neatly-structured monograph includes a detailed introduction and summary of its contents. The included results are presented not only with rigorous proofs but also through numerous intuitive examples. An appendix is provided which includes classic results from the theory of functions and measured sets as well as decision theory, offering an overview of the necessary prerequisites. The formalism of statistical regularities developed in this book will have a significant influence on decision theory and information theory as well as numerous other disciplines. Because of these far-reaching implications, this book may be a useful resource for statisticians, mathematicians, engineers, economists and other utilizing nonstochastic modeling and decision theory.

Synopsis:

This book presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models.

Synopsis:

This book presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models.

Synopsis:

"Decision Systems and Non-stochastic Randomness" presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models. The formalism of statistical regularities developed in this book will have a significant influence on decision theory and information theory as well as numerous other disciplines.

Synopsis:

Decision Systems and Non-stochastic Randomness is the first systematic presentation and mathematical formalization (including existence theorems) of the statistical regularities of non-stochastic randomness. The results presented in this book extend the capabilities of probability theory by providing mathematical techniques that allow for the description of uncertain events that do not fit standard stochastic models. The book demonstrates how non-stochastic regularities can be incorporated into decision theory and information theory, offering an alternative to the subjective probability approach to uncertainty and the unified approach to the measurement of information. This book is intended for statisticians, mathematicians, engineers, economists or other researchers interested in non-stochastic modeling and decision theory.

Table of Contents

Preface.-1. Introduction.-2. Decision Systems.-3. Indifferent Uncertainty.-4. Nonstochastic Randomness.-5. General Decision Problems.-6. Experiment in Decision Problems.-7. Informativity of Experiment in Bayesian Decision Problems.-8. Reducibility of Experiments in Multistep Decision Problems.-9.Concluding Remarks.-A. Mathematical Supplement.-References.-Index.

Product Details

ISBN:
9781441955470
Author:
Ivanenko, V. I.
Publisher:
Springer
Author:
Ivanenko
Subject:
Probability & Statistics - General
Subject:
Game Theory
Subject:
Statistics
Subject:
Mathematics | Probability and Statistics
Subject:
A. Kolmogorov
Subject:
Decision-making
Subject:
Decision Systems
Subject:
Decision Theory.
Subject:
General Decision Problem
Subject:
Probability and Statistics
Subject:
Random Events
Subject:
randomness
Subject:
Stochastic processes
Subject:
Uncertainty
Subject:
Probability Theory and Stochastic Processes
Subject:
Statistical Theory and Methods
Subject:
Game Theory, Economics, Social and Behav. Sciences
Subject:
Operations Research/Decision Theory
Subject:
Statistics for Business/Economics/Mathematical Finance/Insurance
Subject:
Operation Research/Decision Theory
Copyright:
Edition Description:
2010
Publication Date:
20100401
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
284
Dimensions:
240 x 168 mm 653 gr

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Decision Systems and Nonstochastic Randomness New Hardcover
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Product details 284 pages Not Avail - English 9781441955470 Reviews:
"Synopsis" by , This book presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models.
"Synopsis" by , This book presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models.
"Synopsis" by , "Decision Systems and Non-stochastic Randomness" presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models. The formalism of statistical regularities developed in this book will have a significant influence on decision theory and information theory as well as numerous other disciplines.
"Synopsis" by , Decision Systems and Non-stochastic Randomness is the first systematic presentation and mathematical formalization (including existence theorems) of the statistical regularities of non-stochastic randomness. The results presented in this book extend the capabilities of probability theory by providing mathematical techniques that allow for the description of uncertain events that do not fit standard stochastic models. The book demonstrates how non-stochastic regularities can be incorporated into decision theory and information theory, offering an alternative to the subjective probability approach to uncertainty and the unified approach to the measurement of information. This book is intended for statisticians, mathematicians, engineers, economists or other researchers interested in non-stochastic modeling and decision theory.
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