Adaptive significance of winner and loser effects: rank-dependent optimal behaviour
Bibliographic record
Abstract
Many animals including humans show winner and loser effects, whereby contest winners tend to win subsequent fights, while losers tend to lose again. Despite strong empirical evidence for these effects, their adaptive significance, especially that of loser effects, has not been critically examined. We posited that winner and loser effects enable rank-dependent optimal behaviour. Winners' assertive behaviour may deter opponents, and this can increase winners’ access to scarce resources and mates. Losers, in light of their competitive disadvantage, may either switch to alternative strategies in order to enhance their fitness prospects, or wait for competitive conditions to improve before competing again. We critically tested predictions derived from the rank-dependent optimal behaviour hypothesis in two experiments in which we randomly assigned human participants to win or lose in the video game Overwatch. In experiment 1, more losers than winners asked to switch to another video game for the second round. In experiment 2, more losers than winners requested to wait before playing the second round. Mood questionnaires indicated increased positive mood in winners and increased negative mood in losers. We suggest that change in mood is the proximate mechanism underlying rank-dependent optimal behaviour. • The adaptive significance of winner and loser effects is unclear. • We posited that winner and loser effects enable rank-dependent optimal behaviour. • We found that losers were more willing to switch to alternative strategies. • Losers were also more willing to wait for competitive conditions to improve. • Winners showed increased positive mood and loser showed increased negative mood.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".