Giorgio Gilestro
f5f68f5249
A new analytic experiment on an orthogonal evolution-of-sex axis: not the recombination RATE (E9) but the population's mating STRUCTURE. Agents on a ring recombine with a second parent drawn from a window of breadth b (b->0 monogamous/isolation-by-distance, b=1 promiscuous/panmictic), under local selection, swept against NK ruggedness K. Finding: the optimal mate-pool breadth SHRINKS as skills get more entangled. Wide/promiscuous merging wins the champion on additive landscapes (K<=3, b=0.6), but on rugged ones (K>=6) it prematurely converges to a worse champion and an intermediate breadth (b~0.35) wins; pure monogamy over-fragments. Throughout, promiscuity monotonically lifts the population MEAN but destroys diversity and parallel exploration. The design rule extends E9: merge widely for additive skills, keep island-structured sub-populations for entangled ones — a merging-native axis the panmixia-assuming literature lacks. - src/knowledge/mating_system.py + experiment.py dispatch (kind: mating_system) - configs/layer1/E14.yaml (breadth x K sweep, 20 reps, bitwise-reproducible) - figures/plot_E14.py; results/E14/ (figure, README, manifest, resolved config) - tests/test_mating_system.py (+5, 147 green); make layer1 wired - folded into both papers (full + accessible) as the third §5 result Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
||
|---|---|---|
| configs | ||
| figures | ||
| hpc | ||
| paper | ||
| results | ||
| src | ||
| tasks | ||
| tests | ||
| .gitignore | ||
| CLAUDE.md | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
The Lamarckian Society — Layer 1 (analytical core)
A parametric population-genetics model of knowledge transmission across generations of
learning agents. Knowledge transmission is modelled literally as a Wright–Fisher
process (not by analogy): a model's knowledge is a distribution p_t over K discrete
items; a fixed true distribution p* has a rare tail; each generational step is
"sample from the parent (drift) + mix in fresh real samples (grounding/immigration) +
refit." Model collapse is the loss of rare alleles under drift.
See paper/blueprint.md (the normative build spec),
paper/the-lamarckian-society-v5.md (the perspective paper), and
paper/results-summary.md (a summary of all results).
Reproduce
Environment is a uv venv built from the committed, hash-pinned uv.lock — that
lockfile is the single source of truth for "it runs" (Layer 1 is pure NumPy/SciPy and
bitwise-reproducible from a seed; no container needed).
# one-time: install uv (https://astral.sh/uv)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync # build .venv from uv.lock
make test # correctness + scientific-validation tests (the spine of trust)
make layer1 # run experiments E1–E6
make figures # regenerate figures from committed results
Layout
src/knowledge/ Layer 1 package (imported as `knowledge`)
configs/layer1/ one YAML per experiment (E1..E6)
figures/ plot_EX.py — read results.parquet only
tests/ test_correctness.py + test_scientific_validation.py (analytic checks)
paper/ blueprint.md, perspective paper, figure_manifest.md
results/ written artifacts (gitignored; hashes tracked in manifest.json)