Complexity, Entropy and the Physics of Information II
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 2438
Special Issue Editors
Interests: statistical physics; information networks; information economy; complex network
Special Issues, Collections and Topics in MDPI journals
Interests: complexity science; time series analysis; complex network; data mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
An information system is an evolutionary complex system that is widely studied by a large community of interdisciplinary scholars of computer scientists, mathematicians, economists, management and especially physicists. The concepts of complexity, entropy, and the physics of information are interconnected and play crucial roles in understanding the information systems across diverse scientific fields ranging from society, science, biology, engineering, management, etc. Complexity generally refers to the intricate, interconnected, or detailed nature of a system. In various fields, such as science, mathematics, and philosophy, complexity is a multidimensional concept that can involve factors like the number of components, their interactions, and the difficulty of understanding or predicting system behavior. It is related to the physics of information in the sense that information theory provides tools to quantify and analyze the complexity of systems. Entropy is a measure of the amount of disorder or randomness in a system, and it is a measure of uncertainty or unpredictability associated with a set of data in information theory. It is often used to quantify the amount of information or surprise associated with the outcomes of a random variable or a physical system. Thus, the physics of information merges principles from physics and information theory to understand how information is represented, transmitted, and processed in diverse information systems.
Meanwhile, with the rapid development of artificial intelligence (AI) techniques, complexity and entropy play important roles, often influencing the design, performance, and understanding of AI systems. Complexity is crucial for developing models that generalize well to unseen data. Entropy, as a measure of uncertainty, helps in understanding and quantifying the reliability of AI predictions. Balancing complexity and entropy is an ongoing challenge, and various techniques and methodologies are employed to strike an optimal balance for effective and reliable AI systems.
This Special Issue focuses on recent advances in the theories and methods in complexity science and statistical physics and their applications in understanding and analyzing information and AI systems in various scientific disciplines encompassing computer science, physics, biomedicine, management, economics, and more.
Prof. Dr. Yi-Cheng Zhang
Dr. Shimin Cai
Guest Editors
Manuscript Submission Information
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Keywords
- information systems
- neural networks
- information theory
- complexity theory
- statistical physics
- complex networks
- complexity and entropy in AI
- data-driven modelling
- machine learning and deep learning
- time-series analysis
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Related Special Issue
- Complexity, Entropy and the Physics of Information in Entropy (15 articles)
