The Forager’s Dilemma: Food Sharing and Food Defense as Risk‐Sensitive Foraging Options
Bibliographic record
Abstract
Although many variants of the hawk-dove game predict the frequency at which group foraging animals should compete aggressively, none of them can explain why a large number of group foraging animals share food clumps without any overt aggression. One reason for this shortcoming is that hawk-dove games typically consider only a single contest, while most group foraging situations involve opponents that interact repeatedly over discovered food clumps. The present iterated hawk-dove game predicts that in situations that are analogous to a prisoner's dilemma, animals should share the resources without aggression, provided that the number of simultaneously available food clumps is sufficiently large and the number of competitors is relatively small. However, given that the expected gain of an aggressive animal is more variable than the gain expected by nonaggressive individuals, the predicted effect of the number of food items in a clump-clump richness-depends on whether only the mean or both the mean and variability associated with payoffs are considered. More precisely, the deterministic game predicts that aggression should increase with clump richness, whereas the stochastic risk-sensitive game predicts that the frequency of encounters resulting in aggression should peak at intermediate clump richnesses or decrease with increasing clump richness if animals show sensitivity to the variance or coefficient of variation, respectively.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".