Giorgio Gilestro
bab81d3178
Rename layer1-summary.md -> results-summary.md and rewrite to cover the whole body of work with the sexual-reproduction arc as the through-line: Layer 1 (E1-E6, Wright-Fisher core + closed forms), Layer 1.5 (neural existence proof: histogram bridge, RNN metric reframing, architecture- generality, recombination in weights, real-MNIST external validity), the learning kernel (neutral drift fails both neural models oppositely), the sexual society (E7-E11: advantage of sex, Fisher-Muller super-parents, outbreeding depression, directed sex, the dynamic-society vertical claim), and the Riis positioning (concede collapse=drift; lead with the cure). Technical + accessible halves; six design rules. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
||
|---|---|---|
| configs | ||
| figures | ||
| 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) and
paper/the-lamarckian-society-v4.md (the perspective paper).
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)