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Record W4414491377 · doi:10.1063/5.0285101

Collective directional switches of swarming systems with higher-order interactions

2025· article· en· W4414491377 on OpenAlexaff
Shijie Liu, Rui Xiao, Yongzheng Sun

Bibliographic record

VenueChaos An Interdisciplinary Journal of Nonlinear Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsGeneralityCollective behaviorPairwise comparisonSwarming (honey bee)Collective motionControl theory (sociology)Mechanism (biology)

Abstract

fetched live from OpenAlex

Sudden coherent changes in the movement direction are common in animal groups; yet, the mechanism of higher-order and delayed interactions in shaping such collective switching dynamics remains poorly understood. Here, we propose a self-propelled particle model incorporating both pairwise and higher-order social interactions to study the directional switching behaviors in swarming systems, considering scenarios with and without delay. By applying a dimensional reduction method and the Fokker-Planck equation, we obtain the theoretical stationary probability density and the mean switching time. The results reveal that, without time delay, the higher-order interactions significantly increase the mean switching time, promoting stable, ordered movement states and reducing directional switches. When the time delay is introduced, the impact of higher-order interactions becomes non-monotonic. For small delays, they continue to suppress directional switching; for large delays, they instead facilitate more frequent directional switching. This non-monotonic pattern also appears in simulations on realistic social networks, underscoring the generality of the phenomenon. Our study illustrates how higher-order structures and time delays influence collective switching dynamics, highlighting the limitations of pairwise models and the necessity of considering complex interaction networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.314
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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