Diagrammatic expansion of information flows in stochastic Boolean networks

Mori, Fumito; Okada, Takashi

doi:10.1103/physrevresearch.2.043432

Cited by 2 publications

(2 citation statements)

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“…Several prior studies have pointed at the merit of exact results on information-theoretic metrics [25,[29][30][31][32][33][34]. The use of exact theoretical values rather than their statistical estimates alleviates the dependence of any claim on the statistical methods adopted for estimation and brings to light the specific role of model parameters on any information-theoretic metric.…”

Section: Discussionmentioning

confidence: 99%

“…Novelli et al [34] and Goodman and Porfiri [33] independently demonstrated the dependence of transfer entropy on topological properties of network nodes within theoretical studies of a linearly coupled Gaussian model and a Boolean system, respectively. Boolean models have been further investigated in a sequence of studies by some of these authors and others [25,29,32].…”

Section: Discussionmentioning

confidence: 99%

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Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information

2022

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Pairwise interactions are critical to collective dynamics of natural and technological systems. Information theory is the gold standard to study these interactions, but recent work has identified pitfalls in the way information flow is appraised through classical metrics -- time-delayed mutual information and transfer entropy. These pitfalls have prompted the introduction of intrinsic mutual information to precisely measure information flow. However, little is known regarding the potential use of intrinsic mutual information in the inference of directional influences, to diagnose interactions from time-series of individual units. We explore this possibility within a minimalistic, mathematically tractable leader-follower model, for which we document an excess of false inferences of intrinsic mutual information compared to transfer entropy. This unexpected finding is linked to a fundamental limitation of intrinsic mutual information, which suffers from the same sins of time-delayed mutual information: a thin tail of the null distribution that favors the rejection of the null-hypothesis of independence.

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