Bibliographic record
Abstract
Non-local adaptation is the acquisition of general skill at a competitive task. General skill is defined as skill against a broad spectrum of opponents rather than just those an agent encountered during its training or evolution. Biological dogma suggests that evolved creatures should be adapted only to their environment and the opponents they encounter during evolution. If this dogma applies to digital evolution, then we should not observe non-local adaptation in agents trained with evolutionary computation to perform some competitive task. A number of previous studies have found non-local adaptation in prisoner's dilemma, in a model of competitive exclusion in plants, and in a virtual robotics task. This paper examines non-local adaptation in a virtual predator-prey system. One hundred distinct predator-prey lineages are evolved for 250,000 time steps, saving an intermediate population at time step 100,000. Predators and prey from distinct lineages are placed in competition. For the four possible comparisons in which the type of one competitor is held constant and the other is varied from less to more evolved, a statistically significant increase in ability to acquire food is seen in the more-evolved agents.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".