Kin-discriminating partner choice promotes the evolution of helping
Bibliographic record
Abstract
Kin selection theory predicts that individuals should evolve to help relatives, either by helping indiscriminately in a population where they do not move very far from their relatives, or by discriminating kin and conditionally helping them. It has been argued that, because kin discrimination enables individuals to reduce how helpful they are with some social partners as well increase how helpful they are with others, this could lead to an increase or a decrease in the overall level of helping. Specifically, it was argued that kin discrimination would increase the overall level of helping if the function relating the optimal level of help and genetic relatedness is convex, but kin discrimination would decrease the overall level of helping if the function relating the optimal level of help and genetic relatedness is concave. However, this prediction was based on a model in which individuals were not able to choose their social partners but only adjust how helpful they should be toward those social partners they have been allocated. Here, we perform a mathematical analysis showing that being able to choose social partners increases the overall level of helping. Consequently, if kin discriminators are allowed to choose whom they help, kin discrimination is more likely to increase the overall level of helping than previously anticipated. We obtained these results in two complementary theoretical settings: one more general, which makes few demographic assumptions, and the other more specific and concrete, which assumes a patch-structured population with complete dispersal.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".