Giorgio Gilestro
0f7b775ae5
The culmination. A finite population of agents (genotypes, L loci) evolves
on a rugged NK landscape that IS reality (knowledge/dynamic_society.py),
composing the four operators the whole study built toward: grounding,
directed recombination (sex), quality-diversity selection, and mutation.
Grounding is made load-bearing via the consensus-conformity (self-
consumption) mechanism (GG decision): selection acts on
g*true_fitness + (1-g)*conformity, where conformity = agreement with the
population's own consensus, so at g=0 the society optimises fitting-the-
crowd rather than reality.
4-arm ablation (12 reps), each breaking distinctly, only the full society
climbing (global_opt ~ 0.79):
- full 0.78 climbs to the optimum, diversity maintained longest
- no_sex 0.77 can't recombine to escape local optima
- no_diversity 0.74 greedy: collapses diversity fastest, worse local optimum
- no_grounding 0.48 self-consumption collapse to an unfit consensus
(trains on the crowd -> confident-but-wrong mean;
conformity-true gap ~ 0.5)
This integrates E1-E6 + the learning kernel + E7-E10 into one system and
shows the Lamarckian society needs ALL of grounding + directed sex +
diversity: on a rugged landscape you need diversity to explore basins, sex
to recombine them, and grounding to select on reality -- remove any one and
you fail differently. Closes the C3 vertical claim analytically; the LLM
rung remains the eventual empirical instantiation.
New: knowledge/dynamic_society.py, configs/layer1/E11.yaml, figures/
plot_E11.py, README, tests/test_dynamic_society.py (+5). kind:
dynamic_society dispatch; make layer1 wired. 122 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
68 lines
3.1 KiB
Python
68 lines
3.1 KiB
Python
"""E11 figure — the dynamic Lamarckian society: the vertical claim (C3).
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A finite population of agents evolves on a rugged NK landscape (reality). The **full** society —
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grounding + directed recombination (sex) + quality-diversity selection — climbs to the global optimum
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while maintaining diversity longest. A 4-arm ablation shows every operator is load-bearing, each
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breaking distinctly: **no_grounding** collapses to a fit-looking but actually-poor consensus
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(self-consumption); **no_sex** plateaus (can't recombine to escape local optima); **no_diversity**
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(greedy) collapses diversity fastest and stalls at a worse local optimum.
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Three panels over generations: (A) best real capability — the vertical climb, full highest, no_grounding
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crashing below the rest; (B) population diversity — full explores longest, no_grounding collapses
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almost immediately; (C) the self-consumption signature — conformity minus true fitness (how far the
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population's mutual agreement exceeds its real capability), largest for no_grounding. Reads only the
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committed bundle.
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Usage: python figures/plot_E11.py [results/E11]
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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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_ARMS = [("full", "#2ca02c", "full society"),
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("no_sex", "#ff7f0e", "no sex (no recombination)"),
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("no_diversity", "#9467bd", "no diversity (greedy)"),
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("no_grounding", "#d62728", "no grounding (self-consumption)")]
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def main(results_dir: str = "results/E11") -> None:
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df, _ = load_bundle(results_dir)
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arms = [a for a in _ARMS if a[0] in set(df["arm"].unique())]
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g_opt = df["global_opt"].mean()
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fig, axes = plt.subplots(1, 3, figsize=(16, 4.8))
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def traj(ax, col, title, ylabel, hline=None):
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for name, c, lab in arms:
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sub = df[df["arm"] == name]
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g, m, ci = mean_ci(sub, "generation", col)
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ax.plot(g, m, "-", color=c, lw=1.9, label=lab)
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ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.15)
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if hline is not None:
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ax.axhline(hline[0], ls=":", color="gray", lw=1, label=hline[1])
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ax.set(xlabel="generation", ylabel=ylabel, title=title)
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ax.legend(frameon=False, fontsize=8)
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traj(axes[0], "best_fitness", "The vertical climb: general capability\n"
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"(full climbs highest; no-grounding collapses)", "best real fitness",
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hline=(g_opt, "global optimum"))
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traj(axes[1], "diversity", "Specialties maintained: diversity during search\n"
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"(full explores longest; ablations collapse fast)", "population diversity")
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traj(axes[2], "conformity_true_gap", "Self-consumption signature:\n"
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"agreement minus real capability", "conformity − true fitness")
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fig.suptitle("E11 — the dynamic Lamarckian society: grounding + directed sex + diversity climb to "
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"the optimum; remove any one and it breaks (the vertical claim, C3)", y=1.02, fontsize=12)
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fig.tight_layout()
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savefig(fig, results_dir, "E11")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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