society: the dynamic Lamarckian society — the vertical claim (E11 / C3)

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>

figures/plot_E11.py

@@ -0,0 +1,68 @@
+"""E11 figure — the dynamic Lamarckian society: the vertical claim (C3).
+
+A finite population of agents evolves on a rugged NK landscape (reality). The **full** society —
+grounding + directed recombination (sex) + quality-diversity selection — climbs to the global optimum
+while maintaining diversity longest. A 4-arm ablation shows every operator is load-bearing, each
+breaking distinctly: **no_grounding** collapses to a fit-looking but actually-poor consensus
+(self-consumption); **no_sex** plateaus (can't recombine to escape local optima); **no_diversity**
+(greedy) collapses diversity fastest and stalls at a worse local optimum.
+
+Three panels over generations: (A) best real capability — the vertical climb, full highest, no_grounding
+crashing below the rest; (B) population diversity — full explores longest, no_grounding collapses
+almost immediately; (C) the self-consumption signature — conformity minus true fitness (how far the
+population's mutual agreement exceeds its real capability), largest for no_grounding. Reads only the
+committed bundle.
+
+Usage:  python figures/plot_E11.py [results/E11]
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
+
+_ARMS = [("full", "#2ca02c", "full society"),
+         ("no_sex", "#ff7f0e", "no sex (no recombination)"),
+         ("no_diversity", "#9467bd", "no diversity (greedy)"),
+         ("no_grounding", "#d62728", "no grounding (self-consumption)")]
+
+
+def main(results_dir: str = "results/E11") -> None:
+    df, _ = load_bundle(results_dir)
+    arms = [a for a in _ARMS if a[0] in set(df["arm"].unique())]
+    g_opt = df["global_opt"].mean()
+
+    fig, axes = plt.subplots(1, 3, figsize=(16, 4.8))
+
+    def traj(ax, col, title, ylabel, hline=None):
+        for name, c, lab in arms:
+            sub = df[df["arm"] == name]
+            g, m, ci = mean_ci(sub, "generation", col)
+            ax.plot(g, m, "-", color=c, lw=1.9, label=lab)
+            ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.15)
+        if hline is not None:
+            ax.axhline(hline[0], ls=":", color="gray", lw=1, label=hline[1])
+        ax.set(xlabel="generation", ylabel=ylabel, title=title)
+        ax.legend(frameon=False, fontsize=8)
+
+    traj(axes[0], "best_fitness", "The vertical climb: general capability\n"
+         "(full climbs highest; no-grounding collapses)", "best real fitness",
+         hline=(g_opt, "global optimum"))
+    traj(axes[1], "diversity", "Specialties maintained: diversity during search\n"
+         "(full explores longest; ablations collapse fast)", "population diversity")
+    traj(axes[2], "conformity_true_gap", "Self-consumption signature:\n"
+         "agreement minus real capability", "conformity − true fitness")
+
+    fig.suptitle("E11 — the dynamic Lamarckian society: grounding + directed sex + diversity climb to "
+                 "the optimum; remove any one and it breaks (the vertical claim, C3)", y=1.02, fontsize=12)
+    fig.tight_layout()
+    savefig(fig, results_dir, "E11")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])