Investment in the public good through conditional phenotypes of large effect
Bibliographic record
Abstract
We investigate the evolution of an individual's willingness to invest in a public good (what we call, helping) in a patch-structured population with limited natal dispersal. We assume that an individual's decision to invest is informed by its dispersal status: an individual makes one decision given it is native to the patch on which it breeds, and is free to make a different decision given that it is not native to the patch on which it breeds. Unlike previous work, we assume that investment in the public good, and the public good, itself, both have a large effect on individual fecundity. Kin selection analysis reveals that only extreme investment decisions (i.e. 'always invest' or 'never invest') can be evolutionarily stable. Numerical results suggest that the evolutionary instability of the 'never invest' phenotype (what we call, complete nonhelping) implies the evolutionary stability of 'always invest' (what we call, complete helping). In addition, numerical results show that bistability of extreme phenotypes is possible, indicating that the adaptive significance of altruism, in this context, is greater than has been previously recognized. Numerical results are supported by computer simulation, and results, themselves, are briefly discussed in a concluding section.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".