# 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).

```bash
# 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)
```