Giorgio Gilestro
58e6c74609
The review's core instruments adopted: the interpretation/explanation/ prediction ladder is now explicit in §1 (with the decisive pre-merge epistasis-prediction test stated as the open bar, not claimed); identity claims scoped (WF exact only in the minimal model, with the learning-kernel deviation cited against ourselves; Muller's ratchet scoped to the irreversible arm — recombination reassembles only what survives); "nobody has / none imports / theory outrun" removed; merge-don't-average given explicit operator boundaries (output-mean vs weight-average vs routing vs max-with-oracle; budgets; oracle; capacity handoff to speciation); a "what these experiments do and do not establish" scope block added to the speciation section (conflict floor is information-theoretic, not genetic; epistasis-cliff + snowball = hypotheses at the neural tier; emergent DMIs = flagship hypothesis, bounded by our null); "control theory" -> "framework" (subtitle included); §3/§11 overstatements fixed (frozen core != frozen behaviour; Baldwin echo, not identity; operational vs archival irreversibility); claims-at-a-glance table (status/assumptions/evidence/limits) added to §13. Reviewer's framing sentence adopted as the stated core contribution. Accessible version calibrated to match. md2tex gains pipe- table support; PDF rebuilds clean (22 pp). Lessons recorded. 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)