Entropy, Volume 22, Issue 4 (April 2020) – 120 articles
Cover Story (view full-size image): Here, we motivate a geometric perspective of the concept of information flow between components of a complex dynamical system. The most popular methods in this area are probabilistic in nature, including the Nobel-prize-winning work on Granger causality, and also the recently highly popular transfer entropy. Beyond conceptual advancement, a geometric description of causality further allows for new and efficient computational methods of causality inference. In this direction, we introduce a new measure of causal inference based on contrasting fractal correlation dimensions, conditionally applied to compete for explanations of future forecasts. In this setting, we believe our geometric interpretation of information flow has both computational efficiency and theoretical interpretation reasons to contribute positively to many fields of science. View this paper.
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