ArticleScientific reports2016
A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Functional diversity and specialization decoded: Implications for complex particle systems, NeuroAI, and hybrid human-AI ecosystems.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm.Entropy (Basel, Switzerland) · 2023Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Editorial: Inference, Causality and Control in Networks of Dynamical Systems: Data Science and Modeling Perspectives to Network Physiology With Implications for Artificial Intelligence.Frontiers in physiology · 2022Article
- Quantifying emergence and self-organisation of Enterobacter cloacae microbial communities.Scientific reports · 2018Article
- Assessing sustainability in North America's ecosystems using criticality and information theory.PloS one · 2018Article
- Multi-fractal characterization of bacterial swimming dynamics: a case study on real and simulatedProceedings. Mathematical, physical, and engineering sciences · 2017Article
- Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices.Cellular logistics · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.