Measures of Relative Fitness of Social Behaviors in Finite Structured Population Models
Bibliographic record
Abstract
How should we measure the relative selective advantage of different behavioral strategies? The various approaches to this question have fallen into one of the following categories: the fixation probability of a mutant allele in a wild type population, some measures of gene frequency and gene frequency change, and a formulation of the inclusive fitness effect. Countless theoretical studies have examined the relationship between these approaches, and it has generally been thought that, under standard simplifying assumptions, they yield equivalent results. Most of this theoretical work, however, has assumed homogeneity of the population interaction structure--that is, that all individuals are equivalent. We explore the question of selective advantage in a general (heterogeneous) population and show that, although appropriate measures of fixation probability and gene frequency change are equivalent, they are not, in general, equivalent to the inclusive fitness effect. The latter does not reflect effects of selection acting via mutation, which can arise on heterogeneous structures, even for low mutation. Our theoretical framework provides a transparent analysis of the different biological factors at work in the comparison of these fitness measures and suggests that their theoretical and empirical use needs to be revised and carefully grounded in a more general theory.
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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 | 0.000 | 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".