Improving artifact selection via agent migration in multi-population cultural algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-population cultural algorithms are cultural evolutionary frameworks involving multiple independently evolving subpopulations. Artifact selection involves the ability of agents to autonomously reason about selecting artifacts towards achieving their goals. In this study, agent migration between populations in a multi-population cultural algorithm is explored as an approach for augmenting artifact selection knowledge in social agents. Embedded in a social simulation model the multipopulation cultural algorithm consists of two subpopulations where agents in one subpopulation consistently outperform agents in the other due to the presence of knowledge about certain artifacts. Social networks connect agents within a subpopulation and agent knowledge can be altered by members of their network or the best performers of their subpopulation. The model investigates agent migration with novel artifact knowledge from the advanced subpopulation to the underperforming one. Child safety restraint selection is provided as an implemented case study. Results demonstrate the benefits of migration with a higher likelihood of an increase in agent performance when the social network is enabled. The study shows that culturally evolving agents can improve artifact selection knowledge in the absence of standard interventions as a result of migration.
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.
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) | 0.000 | 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 it