Giorgio Gilestro
62c68d6c8c
Enter the Lamarckian society with a robust theoretical frame. The single-
locus, fixed-p* model can only express recovery toward a ceiling; the
society's load-bearing claim is vertical -- capability that EXCEEDS any
component. Generalize knowledge to a distribution over genotypes (L
biallelic loci, K=2^L, additive fitness = # correct loci), reusing all the
K-mode machinery. The one new operator is recombination: free recombination
sends p -> product of per-locus marginals (linkage equilibrium).
E8 (star, kind: society) -- the vertical claim / Fisher-Muller: decorrelated
PARENTS (specialists, expert on their loci, agnostic elsewhere) are
recombined; sexual merge assembles a genotype fitter than any parent,
climbing to the optimum (12/12, a genotype no parent had) as parent count
grows and rho->0, while the best single parent (~8.7) and the mean-mixture
"model soup" (~11.6) plateau below. Reuses make_retention_matrix (locus
mastery replaces tail-item retention).
E7 (kind: genotype_lineage) -- the advantage of sex: a single population
adapts toward the optimum; the sexual lineage adapts faster than asexual
(clonal interference) by keeping loci in linkage equilibrium (LD->0 vs LD
spike). Honest scope: a speed advantage, not a permanent Muller's-ratchet
gap (subtle to force); E8 carries the headline.
Metaphor shift (per GG): the society is sexual reproduction with UNBOUNDED
parents, not teacher->pupil. Teacher->pupil caps at the ceiling; n-parent
recombination is combinatorial and generative, and unlike biology there is
no two-parent limit. Collapse = asexual degradation; the cure = sex. This
unifies E4 (merge != average) + E6 (irreversibility) under evolution-of-sex
theory and reaches ground Riis's single-locus n-grams cannot.
New: knowledge/{genotype,genotype_lineage,society}.py, configs/layer1/{E7,
E8}.yaml, figures/plot_{E7,E8}.py, READMEs, tests/test_genotype.py (+7).
experiment.py dispatch (kind in {genotype_lineage, society}); make layer1
wired. 112 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
"""E7 figure — the advantage of sex: recombination adapts faster than clonal reproduction.
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The dynamic mechanism behind E8. A single population adapts from all-wrong toward a multi-locus
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optimum under selection + drift + mutation. Beneficial alleles arise in different sub-lineages;
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sexual recombination reassorts them into one genotype, while an asexual lineage suffers clonal
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interference. The sexual lineage climbs faster — the classical advantage of sex (an honest *speed*
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advantage; both eventually plateau near the optimum in this tractable regime).
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Two panels: (A) mean-fitness adaptation curves, asexual vs sexual, over generations; (B) linkage
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disequilibrium over generations — asexual holds beneficial alleles in disequilibrium (scattered
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across genotypes) while sexual drives it to ~0 (assembled), the mechanism of the speed gap.
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Usage: python figures/plot_E7.py [results/E7]
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import matplotlib.pyplot as plt
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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, mean_ci, savefig # noqa: E402
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def main(results_dir: str = "results/E7") -> None:
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df, cfg = load_bundle(results_dir)
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L = cfg["genotype"]["L"]
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arms = [(0.0, "#7f7f7f", "asexual (clonal)"), (1.0, "#d62728", "sexual (recombining)")]
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fig, axes = plt.subplots(1, 2, figsize=(13, 5))
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ax = axes[0]
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for rate, c, lab in arms:
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sub = df[df["recomb_rate"] == rate]
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g, m, ci = mean_ci(sub, "generation", "mean_fitness")
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ax.plot(g, m, "-", color=c, lw=1.8, label=lab)
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ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.2)
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ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
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ax.set(xlabel="generation", ylabel="mean fitness (# correct loci)",
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title="Advantage of sex: recombination adapts faster\n(clonal interference slows the asexual lineage)")
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ax.legend(frameon=False, fontsize=9)
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ax = axes[1]
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for rate, c, lab in arms:
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sub = df[df["recomb_rate"] == rate]
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g, m, ci = mean_ci(sub, "generation", "ld")
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ax.plot(g, m, "-", color=c, lw=1.8, label=lab)
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ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.2)
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ax.set(xlabel="generation", ylabel="mean linkage disequilibrium |D|",
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title="Mechanism: asexual scatters beneficial alleles (LD>0);\nsexual assembles them (LD→0)")
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ax.legend(frameon=False, fontsize=9)
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fig.suptitle("E7 — the advantage of sex: recombination reassorts beneficial alleles that arose "
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"in different lineages", y=1.02, fontsize=12)
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fig.tight_layout()
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savefig(fig, results_dir, "E7")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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