To follow or not? How animals in fusion–fission societies handle conflicting information during group decision‐making
Bibliographic record
Abstract
When group members possess differing information about the environment, they may disagree on the best movement decision. Such conflicts result in group break-ups, and are therefore a fundamental driver of fusion-fission group dynamics. Yet, a paucity of empirical work hampers our understanding of how adaptive evolution has shaped plasticity in collective behaviours that promote and maintain fusion-fission dynamics. Using movement data from GPS-collared bison, we found that individuals constantly associated with other animals possessing different spatial knowledge, and both personal and conspecific information influenced an individual's patch choice decisions. During conflict situations, bison used group familiarity coupled with their knowledge of local foraging options and recently sampled resource quality when deciding to follow or leave a group - a tactic that led to energy-rewarding movements. Natural selection has shaped collective behaviours for coping with social conflicts and resource heterogeneity, which maintain fusion-fission dynamics and play an essential role in animal distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".