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)