Food unpredictability drives both generalism and social foraging: a game theoretical model
Why this work is in the frame
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Bibliographic record
Abstract
Resource predictability can influence foraging behavior in many ways. Depending on the predictability of food sources, animals may specialize on a few food types or generalize on many; they may aggressively defend feeding territories or nonaggressively share food with others. However, food defense and diet breadth have generally been studied separately. In this paper, we propose that variation in resource predictability could drive both of them together. We construct a game theoretic model to test whether situations in which resources are unpredictable might favor both generalism (the ability to use multiple food types) and nonaggressive social foraging. Our model predicts that the proportion of social generalists is highest when resources are unpredictable, whereas a predictable resource distribution favors territorial specialists. We discuss our result within the context of animal cognition research, where diet breadth and social foraging are associated with the 2 dominant views of the evolution of cognition: the “ecological” and the “social brain” hypotheses. Our results suggest that social and dietary demands on cognition might be less independent than is often assumed.
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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 | 0.001 |
| 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 it