Partner choice increases observed reciprocity-based cooperation but decreases unobserved stake-based cooperation
Bibliographic record
Abstract
According to current theory and experiments, cooperation is more likely to evolve when organisms can choose to replace uncooperative partners with cooperative ones. However, there is a downside to this partner choice: when partners can be easily replaced, organisms have less stake in their partners' welfare and will therefore be less likely to help keep those partners alive and well enough to reciprocate. Here, I present a mathematical model showing that when a third party is present, organisms will provide more observable help to their partners (reciprocity/signalling-based helping), but less anonymous help that would keep that partner in good condition (stake-based helping). The net effect of partner choice depends on the relative strength of these two factors: partner choice has a more positive effect if interactions are short (i.e. less stake), when observers judge based on observed helping (i.e. reputation matters), and when one can have multiple cooperative partners at the same time. These results show the importance of differentiating between helping that relies on observation (e.g. reciprocity and signalling), helping that requires no observation (e.g. kinship and stake), and how the two types interact.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".