The Kin Composition of Social Groups: Trading Group Size for Degree of Altruism
Bibliographic record
Abstract
Why some social systems form groups composed of kin, while others do not, has gone largely untreated in the literature. Using an individual-based simulation model, we explore the demographic consequences of making kinship a criterion in group formation. We find that systems where social groups consist of one-generation breeding associations may face a serious trade-off between degree of altruism and group size that is largely mediated by their kin composition. On the one hand, restricting groups to close kin allows the evolution of highly altruistic behaviors but may limit group size to suboptimal levels, the more severely so the smaller the intrinsic fecundity of the species and the stricter the kin admission rule. Group size requirements, on the other hand, can be met by admitting nonkin into groups, but not without limiting the degree of altruism that can evolve. As a solution to this conundrum, we show that if helping roles within groups are assigned through a lottery rather than being genetically determined, maximum degrees of altruism can evolve in groups of nonrelatives of any size. Such a "lottery" mechanism may explain reproductive and helping patterns in organisms as varied as the cellular slime molds, pleometrotic ants, and Galapagos hawks.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| 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".