Signalling Victory to Ensure Dominance: A Continuous Model
Bibliographic record
Abstract
A possible rationale for victory displays—which are performed by the winners of contests but not by the losers—is that the displays are attempts to decrease the probability that the loser of a contest will initiate a future contest with the same individual. We explore the logic of this “browbeating” rationale with a game-theoretic model, which extends previous work by incorporating the effects of contest length and the loser’s strategic response. The model predicts that if the reproductive advantage of dominance over an opponent is sufficiently high, then, in a population adopting the evolutionarily stable strategy or ESS, neither winners nor losers signal in contests that are sufficiently short; and only winners signal in longer contests, but with an intensity that increases with contest length. These predictions are consistent with the outcomes of recent laboratory studies, especially among crickets, where there is now mounting evidence that eventual winners signal far more frequently than losers after fighting, and that post-conflict displays are more likely to be observed after long contests.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".