Using Reproductive Altruism to Evolve Multicellularity in Digital Organisms
Bibliographic record
Abstract
The processes by which multicellular organismsfirst emerged from their unicellular ancestors are fundamental to the biology of complex, differentiated life forms. Previous work suggests that reproductive division of labor between specialized germ and soma cells was central to this evolution in some cases. Here, we assess the potential of the digital life platform Avida to examine the trade-off between survival and replication in multicellular organisms. Avida uses a grid of selfreplicating computer programs capable of mutation and evolution to address biological questions computationally. We model our digital organisms after the Volvocales, a flagellated order of photosynthetic green algae that includes both unicellular and multicellular species. We show that, given selective pressures similar to those experienced by the Volvocales in nature, digital organisms are capable of evolving multicellularity within the Avida platform. The strategies we observed that best handled the trade-off between survival and replication involved germ cells producing sterile, somatic offspring. These strategies are similar to those observed in volvocine algae, which suggests that digital platforms, such as Avida, are appropriate to use in the study of reproductive altruism.
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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 | 0.001 | 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 | 0.000 | 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".