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>

results/E11/manifest.json

@@ -0,0 +1,14 @@
+{
+  "experiment": "E11",
+  "master_seed": 20260705,
+  "git_commit": "48181a1c846f41e9254abe4b1d0f843d1df9667f",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 3888,
+  "results_sha256": "e8d2c3ecb5200578d16a977d147b6ee05ebf83e1a81d9287f6a740114ef3968a"
+}