Giorgio Gilestro
612be58433
The Introduction is halved (1,360 -> 654 words, four paragraphs): the model-population setting; the data-coupled generations + the thesis sentence; the drift diagnosis placed in the literature; and the motivating question (the four operator decisions with no principled guidance + the continual-learning framing), closing on the value anticipation without disclosing results. Evicted and rehomed: the interpretation/explanation/prediction ladder (deleted — its content lives in the calibrated Results and ledger); the answers-list (deleted — results belong in Results); the correspondence walk-through (Muller's ratchet moved to the minimal-model section with its scope clause; immigration/Fisher-Muller/BDM citations anchored where the concepts are developed in Results; the Livnat precursor and predictor-delta moved to the Discussion ledger); the tiers-of-evidence and negative-results- prominence sentences (deleted). The continual-learning operator mapping moved into the Discussion block, retitled "Continual learning at the population scale", deduplicated against its five offers. All 66 references wholesale-renumbered to the new first-appearance order and the list reordered (invariant verified: in-text order = 1..66 = list). Main text 4.7k words; 19 pp. 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)