Decision Systems and Nonstochastic Randomness
“Decision Systems and Nonstochastic Randomness” by V. I. Ivanenko is a comprehensive exploration of the mathematical formalization of non-stochastic randomness, published by Springer New York in April 2010. This 272-page edition presents a systematic approach to the statistical regularities of non-stochastic randomness, extending the capabilities of traditional probability theory. The book focuses on providing mathematical techniques that describe uncertain events outside standard stochastic models.
Readers will find a detailed examination of how non-stochastic regularities can be integrated into decision theory and information theory, offering an alternative perspective on uncertainty. The content is particularly relevant for statisticians, mathematicians, engineers, and economists, as it delves into non-stochastic modeling and decision-making processes. This work aims to enhance understanding in fields such as probability and statistics, business mathematics, and operations research, making it a valuable resource for researchers interested in these topics.
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“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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