Giorgio Gilestro
073fc33509
The CS reader is now walked into the biology as it arrives: drift (surname extinction, island alleles), Wright-Fisher and heterozygosity defined in place (collision-probability reading), Muller's ratchet (Y-chromosome decay), immigration (the one-migrant-per-generation rule of conservation management), blending inheritance (Jenkin's 1867 swamping argument, which the Proposition makes exact), Fisher-Muller (clonal competition vs assembly), genotype/locus, epistasis and NK landscapes glossed, outbreeding depression (the Tatra ibex restocking collapse), panmixia, BDM incompatibilities (mule sterility), hybrid load. Three new literature anchors (Mills & Allendorf 1996; Jenkin 1867; Templeton 1986), all verified; references renumbered to first-appearance order (now 72) and re-verified 1..72. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v |
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| paper | ||
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| CLAUDE.md | ||
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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)