Rapid Exploiter‐Victim Coevolution: The Race Is Not Always to the Swift
Bibliographic record
Abstract
The modeling of coevolutionary races has traditionally been dominated by methods invoking a timescale separation between ecological and evolutionary dynamics, the latter assumed to be much slower than the former. Yet it is becoming increasingly clear that in many cases the two processes occur on similar timescales and that such "rapid" evolution can have profound implications for the dynamics of communities and ecosystems. After briefly reviewing the timescale separations most common in coevolution theory, we use a general model of exploiter-victim coevolution to confront predictions from slow-evolution analysis with Monte Carlo simulations. We show how rapid evolution radically alters the dynamics and outcome of coevolutionary arms races. In particular, a fast-evolving exploiter can enable victim diversification and thereby lose a race it is expected to win. We explain simulation results, using mathematical analysis with relaxed timescale separations. Unusual mutation parameters are not required, since rapid evolution naturally emerges from slow competitive exclusion. Our results point to interesting consequences of exploiter rapid evolution and experimentally testable patterns, while indicating that more attention should be paid to rapid evolution in evolutionary 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".