Learning behaviorally stable solutions to producer–scrounger games
Bibliographic record
Abstract
Animal decision making is influenced by past experience in many biological contexts such as mating, avoiding predators, and foraging. In behavioral games, what constitutes a good or bad decision about which alternative to use depends on the behavior of other individuals. Solutions to games can take the form of a stable equilibrium frequency (SEF) of alternative tactics. In this study, we ask whether individuals within flocks of ground-feeding passerines (Lonchura punctulata) engaged in a producer–scrounger game adjust their behavior and converge on the SEF by using a fixed rule of thumb or by learning to estimate payoffs by the process of responding to contingencies in reinforcements obtained from each alternative tactic. After being trained either in a high-scrounging (HS) or a low-scrounging (LS) food condition, flocks of birds were provided with identical foraging conditions over 2 successive test phases in which we expected the SEF of scrounging to decrease and then to increase. Birds trained in the HS condition scrounged more than those trained in the LS condition and continued to do so even when subsequently tested in the same conditions. This effect of past experience is inconsistent with the use of a fixed rule alone. An improvement in the efficiency of scrounging behavior within experimental phases provided further evidence of learning in this game-theoretic context. This experiment provides the first empirical evidence that group-level adjustments in scrounger use to different environmental conditions are mediated by learning the payoffs associated with each tactic.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".