Collective directional switches of swarming systems with higher-order interactions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".