Bibliographic record
Abstract
The use of tools or artifacts is essential to the human race and has been the subject of recent research in Artificial Intelli-gence. How individual agents acquire these capabilities and how they evolve can be considered vital steps towards under-standing complex group capabilities. In a previous study, we designed and implemented an extended version of a theoret-ical model for artifact capability that accommodated biolog-ical evolution and learning via exploratory methods. Histor-ical knowledge and genetic algorithms were combined with learning techniques to build agents that could learn either in-dividually from observations of their own behaviour or so-cially by observation from a distance. In this study, we in-corporate a collaborative form of cultural learning into the model in an effort to enhance the artifact capability-learning agents. This is accomplished via the design of a cultural evo-lutionary model that utilizes genetic and cultural algorithms to complement the cognitive abilities of the agents. Learning agents belonging to a social network cooperate with and ben-efit from each other by sharing individual experiences. Re-sults obtained from the multi-agent simulation implementa-tion confirm the efficiency of social learning over individual learning and demonstrate the benefits of cultural over biolog-ical evolution. They also suggest that as artifacts get more complex, social agents learning via cultural influence outper-form those learning by observation from a distance.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".