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Bundled Causal History Interaction

Ven Te Chow Hydrosystem Laboratory, Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USA
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Entropy 2020, 22(3), 360; https://doi.org/10.3390/e22030360
Received: 19 February 2020 / Revised: 14 March 2020 / Accepted: 18 March 2020 / Published: 20 March 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of pH in an observed stream chemistry system. In the studied catchment, the dynamics of pH is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems. View Full-Text
Keywords: bundled causal dynamics; information measures; complex system bundled causal dynamics; information measures; complex system
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Jiang, P.; Kumar, P. Bundled Causal History Interaction. Entropy 2020, 22, 360.

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