Learning to select mates in artificial life
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
Artificial life (A-life) simulations present a natural way to study interesting phenomena emerging in a population of evolving agents. In this paper, we investigate whether allowing A-life agents to select mates can extend the lifetime of a population. In our approach, each agent evaluates potential mates via a preference function. The role of this function is to map information about an agent and its candidate mate to a scalar preference for deciding whether or not to form an offspring. We encode the parameters of the preference function genetically within each agent, thus allowing such preferences to be agent-specific as well as evolving over time. We evaluate this approach in a simple predator-prey A-life environment and demonstrate that the ability to evolve a per-agent mate-selection preference function indeed significantly increases the extinction time of the population. Additionally an inspection of the evolved preference function parameters shows that agents evolve to favor mates who have survival traits.
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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 it