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Abstract 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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A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems
Abstract Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units’ individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units’ time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the sources and targets individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community.
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- PAR ID:
- 10426337
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Physics: Complexity
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2632-072X
- Format(s):
- Medium: X Size: Article No. 025020
- Size(s):
- Article No. 025020
- Sponsoring Org:
- National Science Foundation
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