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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".