Giorgio Gilestro
f82d11ccce
The convergence paragraph rewritten as a natural literature entry: the
drift identification is stated as a fact of the field, made repeatedly
and independently (pre-deep-learning inference chains; LLM text
ecosystems; the first-extinction law; quantitative-genetic form), its
multiplicity presented as a property of the idea rather than a claim
about us; the pivot is positive (population genetics is a theory of what
maintains populations despite decay, and this paper develops that fuller
structure) instead of defensive ("what none of that parallel work
develops"). "We reached independently", "priority of publication", and
"convergence we take as support" removed from the abstract and the
Discussion ledger as well. Refs 22-25 renumbered to the new textual
(chronological) order; citation invariant re-verified (1..66). Lesson
recorded: internal strategic deliberations must not surface in
reader-facing prose — confident papers situate, they do not litigate.
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)