Food unpredictability drives both generalism and social foraging: a game theoretical model
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.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".