Manuscript Topics

Statistical complexity measures the amount of structure or patterns in a system, capturing how much information is needed to describe its behavior. In natural systems, like weather patterns or ecosystems, statistical complexity arises from the interaction of many variables, leading to intricate and often unpredictable dynamics. These systems display rich, emergent behaviors that are challenging to model but are deeply informative about the underlying processes that govern them.

In artificial intelligence (AI), statistical complexity plays a crucial role in understanding how machine learning models interpret and process data. AI systems aim to capture the patterns in data—such as images, text, or behaviors— with the goal of making predictions or decisions. By minimizing unnecessary complexity while maximizing predictive power, AI seeks to balance simplicity with the ability to accurately model the complex structures found in natural systems, leading to more efficient and effective algorithms.

We invite submissions for a special issue on Statistical Complexity in Natural Systems and Artificial Intelligence. This issue seeks to explore innovative approaches that apply statistical complexity to uncover patterns and structures in both natural and artificial systems. Topics of interest include, but are not limited to, complexity in ecosystems, climate models, biological networks, as well as machine learning, neural networks, and AI algorithms. We welcome theoretical, empirical, and applied research that demonstrates how understanding complexity can lead to advancements in modeling, prediction, and optimization in these domains.

Topics:
• Statistical Complexity
• Disorder Indicators
• Order Quantifiers
• Information Theory
Applications in:
Classical and Quantum Systems, Dynamical Systems, Multi-Agent Systems, Time Signals, Data Science, Artificial Intelligence, etc.

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