Social evolution in the shadow of asymmetrical relatedness
Bibliographic record
Abstract
The persistence of altruism and spite remains an enduring problem of social evolution. It is well known that selection for these actions depends on the structure of the population-that is, on actors' genetic relationships to recipients and to the 'neighbourhood' upon which the effects of their actions redound. Less appreciated, however, is that population structure can cause genetic asymmetries between partners whereby the relatedness (defined relative to the neighbourhood) of an individual i to a partner j will differ from the relatedness of j to i. Here, we introduce a widespread mechanism of kin recognition to a model of dispersal in subdivided populations. In so doing, we uncover three remarkable consequences of asymmetrical relatedness. First, altruism directed at phenotypically similar partners evolves more easily among migrant than native actors. Second, spite directed at dissimilar partners evolves more easily among native than migrant actors. Third, unlike migrants, natives can evolve to pay costs that far outstrip those they spitefully impose on others. We find that the frequency of natives relative to migrants amplifies the asymmetries between them. Taken together, our results reveal differentiated patterns of 'phenocentrism' that readily arise from asymmetries of relatedness.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".