Social interactions generate complex selection patterns in virtual worlds
Bibliographic record
Abstract
Understanding the influence of social interactions on individual fitness is key to improving our predictions of phenotypic evolution. However, we often overlook the different components of selection regimes arising from interactions among organisms, including social, correlational, and indirect selection. This is due to the challenging sampling efforts required in natural populations to measure phenotypes expressed during interactions and individual fitness. Furthermore, behaviours are crucial in mediating social interactions, yet few studies have explicitly quantified these selection components on behavioural traits. In this study, we capitalize on an online multiplayer videogame as a source of extensive data recording direct social interactions among prey, where prey collaborate to escape a predator in realistic ecological settings. We estimate natural and social selection and their contribution to total selection on behavioural traits mediating competition, cooperation, and predator-prey interactions. Behaviours of other prey in a group impact an individual’s survival, and thus are under social selection. Depending on whether selection pressures on behaviours are synergistic or conflicting, social interactions enhance or mitigate the strength of natural selection, although natural selection remains the main driving force. Indirect selection through correlations among traits also contributed to the total selection. Thus, failing to account for the effects of social interactions and indirect selection would lead to a misestimation of the total selection acting on traits. Dissecting the contribution of each component to the total selection differential allowed us to investigate the causal mechanisms relating behaviour to fitness and quantify the importance of the behaviours of conspecifics as agents of selection. Our study emphasizes that social interactions generate complex selective regimes even in a relatively simple ecological environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".