Economic contract theory tests models of mutualism
Bibliographic record
Abstract
Although mutualisms are common in all ecological communities and have played key roles in the diversification of life, our current understanding of the evolution of cooperation applies mostly to social behavior within a species. A central question is whether mutualisms persist because hosts have evolved costly punishment of cheaters. Here, we use the economic theory of employment contracts to formulate and distinguish between two mechanisms that have been proposed to prevent cheating in host-symbiont mutualisms, partner fidelity feedback (PFF) and host sanctions (HS). Under PFF, positive feedback between host fitness and symbiont fitness is sufficient to prevent cheating; in contrast, HS posits the necessity of costly punishment to maintain mutualism. A coevolutionary model of mutualism finds that HS are unlikely to evolve de novo, and published data on legume-rhizobia and yucca-moth mutualisms are consistent with PFF and not with HS. Thus, in systems considered to be textbook cases of HS, we find poor support for the theory that hosts have evolved to punish cheating symbionts; instead, we show that even horizontally transmitted mutualisms can be stabilized via PFF. PFF theory may place previously underappreciated constraints on the evolution of mutualism and explain why punishment is far from ubiquitous in nature.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".