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This study presents a novel scientific paradigm using Data and Artificial Adaptive Systems to analyze complex processes. It reveals hidden event connections through spatial distribution analysis.

Keywords:
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Area of Science:

  • Interdisciplinary science
  • Complexity science
  • Data analysis

Background:

  • Understanding natural and cultural processes often involves complex, nonlinear interactions.
  • Traditional analytical methods may struggle to capture the inherent nonlinearity in event distributions.

Purpose of the Study:

  • Introduce a new scientific paradigm for analyzing natural and cultural processes.
  • Demonstrate how data and Artificial Adaptive Systems can model nonlinearity.
  • Explore the relationship between spatial event distribution and underlying event logic.

Main Methods:

  • Utilizing Data as representative samples of processes.
  • Employing Artificial Adaptive Systems (AAS) as a mathematical technique.
  • Analyzing the spatial distribution of events to uncover connections.

Main Results:

  • The proposed paradigm offers a powerful framework for process analysis.
  • Artificial Adaptive Systems effectively reveal embedded nonlinearity.
  • Spatial event distribution patterns can elucidate hidden causal links.

Conclusions:

  • A new paradigm integrating Data and Artificial Adaptive Systems enhances process understanding.
  • This approach is effective for uncovering complex relationships in event data.
  • Spatial analysis is key to understanding the logic connecting events.