Dear Colleagues,

Data analysis under uncertainty and decision making with imperfect information are basic tasks of intelligent systems, both biological and artificial. In such tasks, the functionality and efficiency of the intelligent system depends heavily on the selected methods for uncertainty analysis and information processing.

Starting from the basic ideas by Fermat and Pascal, a main quantitative measure that is used for representing uncertainty is probability. A main theory for handling this measure is provided by Kolmogorov probability theory, which was axiomatically formalized only in 1934. Later, in the 1960s, intensive scientific debate contributed to a better understanding of the nature of probability and its mathematical basis. Modern probability theory and Bayesian decision making followed as rigorous techniques for treating and addressing uncertainty-related concepts. On this topic, and on the basis of probability theory concepts, Shannon introduced information theory as a mathematical theory of communication systems (1948), which played a key role in the progress of communication and computational machinery.

Along with the development of complex systems such as production lines, industrial and service systems, in which both humans and machines interacted, it became clear that probabilistic and information theory models can result in erroneous conclusions about human and human–machine activities.

In order to overcome this problem, starting from the 1950s, several non-Aristotelian methods of reasoning and non-probabilistic measures of uncertainty were developed. For example, in 1958 Lambek suggested non-commutative logic that represents the syntax of sentences in natural languages, and in 1965, Zadeh introduced the notion of fuzzy sets and, consequently, fuzzy logic that allows direct description of non-Bayesian human reasoning. Formally, these studies can be considered as an indirect continuation of the 1920s formalization of semantics by Lukasiewicz, as well as the works by Lukasiewicz and Tarski in multivalued logic during the 1930s. In 1979, Kahneman and Tversky discussed the difference between the reasoning based on rational principals to the reasoning of individual human decision making; however, a complete theory of such irrationality has not yet been formally established.

In this Special Issue, we invite papers that present original results, both theoretical and empirical, in the field of information and uncertainty analysis, as well as decision making in human–machine systems. We seek papers that consider applications related to modern industrial and service systems. In particular, this Issue welcomes papers that deal with the gap and the compliance between the decision-making process of intelligent rational systems vs. the reasoning process of human behavior, which is not necessarily rational. Theoretical results related to information and entropy measures that support rational and irrational decision making with strong applicability are welcomed.

Tentative topics include the implementation of information and uncertainty concepts and tools to:

• Human–machine systems;
• Big Data and analytic systems;
• Machine learning algorithms;
• Sharing economy/industry as a service;
• Autonomous vehicles/agents/robots;
• Data-driven models and services;
• Smart cities and logistics;
• Data-driven sustainable planning and operations;
• Safety, privacy and fairness.

Prof. Dr. Irad E. Ben-Gal
Prof. Dr. Parteek Kumar Bhatia
Dr. Eugene Kagan
Guest Editors

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• information theory
• uncertainty analysis
• analytic systems
• artificial intelligence
• agent-based systems
• machine learning
• decision-making
• stochastic optimization
• probabilistic control