Giorgio Gilestro
bf4b1c077c
paper/pnas/main.md — the manuscript restructured as a research article (~5.6k words main text): significance statement, abstract, introduction (diagnosis conceded; the management thesis; the interpretation/ explanation/prediction ladder with the prediction rung stated as a bounded controlled test), the minimal model with its exactness boundary (learning kernel cited against ourselves), Table 1 dictionary with per-row support levels, a five-step results ladder (grounding floor; conservation law + operator boundaries + Fisher-Muller + directed sex + mating structure; the jointly-necessary society; speciation across three tiers with the emergent null; the controlled predictive test at second-review calibration), discussion (design rules, borrowed-vs-ours ledger, limits with the reviewer's generalisation-before-scale ordering, what biology gets back), brief methods, 30 references. build.py composes 6 figures by stacking committed vector PDFs (bespoke unified re-plots deferred to submission polish); builds clean under tectonic (15 pp incl. 6 full-page figures). si.md: SI skeleton (propositions, claims ledger, per-tier methods, statistics, figure list). Manifesto sections of v6 (institutions, timescales, re-minting) compressed into Discussion per the plan; v6 remains the long-form perspective document. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v |
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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)