Rethinking Mutualism Stability: Cheaters and the Evolution of Sanctions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How cooperation originates and persists in diverse species, from bacteria to multicellular organisms to human societies, is a major question in evolutionary biology. A large literature asks: what prevents selection for cheating within cooperative lineages? In mutualisms, or cooperative interactions between species, feedback between partners often aligns their fitness interests, such that cooperative symbionts receive more benefits from their hosts than uncooperative symbionts. But how do these feedbacks evolve? Cheaters might invade symbiont populations and select for hosts that preferentially reward or associate with cooperators (often termed sanctions or partner choice); hosts might adapt to variation in symbiont quality that does not amount to cheating (e.g., environmental variation); or conditional host responses might exist before cheaters do, making mutualisms stable from the outset. I review evidence from yucca-yucca moth, fig-fig wasp, and legume-rhizobium mutualisms, which are commonly cited as mutualisms stabilized by sanctions. Based on the empirical evidence, it is doubtful that cheaters select for host sanctions in these systems; cheaters are too uncommon. Recognizing that sanctions likely evolved for functions other than retaliation against cheaters offers many insights about mutualism coevolution, and about why mutualism evolves in only some lineages of potential hosts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it