Giorgio Gilestro
a6eb9b7512
Scaffold plus the Layer 1 analytical core and the first two experiments. - knowledge/: truth, metrics, teachers (2.7.1 shared-switch construction), step, lineage, experiment, config, seeding (imported as `knowledge`). - Validation spine green: neutral decay (Pred 1), fixation (Pred 2), exact mutation-drift equilibrium (Pred 3), union coverage (Pred 5). 68 tests pass. - E1 reproduces tail-first collapse. E2 delivers the headline: a grounding phase boundary g* << 1, with stationary H tracking the exact H_eq closed form (g=0.005 -> 68% of truth diversity; g=0.05 -> 96%). - Reproducibility: uv venv from a hash-pinned uv.lock is the source of truth; every run writes results.parquet + resolved_config.yaml + manifest.json (lib versions, git commit, sha256). Figures and manifests tracked; the large regenerable parquet is gitignored. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
# The Lamarckian Society — Layer 1 (analytical core)
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A parametric population-genetics model of knowledge transmission across generations of
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learning agents. Knowledge transmission is modelled *literally* as a Wright–Fisher
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process (not by analogy): a model's knowledge is a distribution `p_t` over `K` discrete
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items; a fixed true distribution `p*` has a rare tail; each generational step is
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"sample from the parent (drift) + mix in fresh real samples (grounding/immigration) +
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refit." Model collapse is the loss of rare alleles under drift.
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See `paper/blueprint.md` (the normative build spec) and
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`paper/the-lamarckian-society-v4.md` (the perspective paper).
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## Reproduce
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Environment is a `uv` venv built from the committed, hash-pinned `uv.lock` — that
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lockfile is the single source of truth for "it runs" (Layer 1 is pure NumPy/SciPy and
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bitwise-reproducible from a seed; no container needed).
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```bash
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# one-time: install uv (https://astral.sh/uv)
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv sync # build .venv from uv.lock
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make test # correctness + scientific-validation tests (the spine of trust)
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make layer1 # run experiments E1–E6
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make figures # regenerate figures from committed results
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```
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## Layout
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```
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src/knowledge/ Layer 1 package (imported as `knowledge`)
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configs/layer1/ one YAML per experiment (E1..E6)
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figures/ plot_EX.py — read results.parquet only
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tests/ test_correctness.py + test_scientific_validation.py (analytic checks)
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paper/ blueprint.md, perspective paper, figure_manifest.md
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results/ written artifacts (gitignored; hashes tracked in manifest.json)
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```
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