E14: mating systems — monogamy vs promiscuity (mate-pool breadth)

A new analytic experiment on an orthogonal evolution-of-sex axis: not the
recombination RATE (E9) but the population's mating STRUCTURE. Agents on a
ring recombine with a second parent drawn from a window of breadth b
(b->0 monogamous/isolation-by-distance, b=1 promiscuous/panmictic), under
local selection, swept against NK ruggedness K.

Finding: the optimal mate-pool breadth SHRINKS as skills get more
entangled. Wide/promiscuous merging wins the champion on additive
landscapes (K<=3, b=0.6), but on rugged ones (K>=6) it prematurely
converges to a worse champion and an intermediate breadth (b~0.35) wins;
pure monogamy over-fragments. Throughout, promiscuity monotonically lifts
the population MEAN but destroys diversity and parallel exploration. The
design rule extends E9: merge widely for additive skills, keep
island-structured sub-populations for entangled ones — a merging-native
axis the panmixia-assuming literature lacks.

- src/knowledge/mating_system.py + experiment.py dispatch (kind: mating_system)
- configs/layer1/E14.yaml (breadth x K sweep, 20 reps, bitwise-reproducible)
- figures/plot_E14.py; results/E14/ (figure, README, manifest, resolved config)
- tests/test_mating_system.py (+5, 147 green); make layer1 wired
- folded into both papers (full + accessible) as the third §5 result

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

figures/plot_E14.py

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+"""E14 figure — mating systems: monogamy vs promiscuity (mate-pool breadth) across ruggedness.
+
+Three panels, each vs mate-pool breadth (log x: 0.03 = monogamous/structured -> 1.0 = promiscuous/
+panmictic), one line per landscape ruggedness K:
+
+(A) best fitness / global optimum — the *champion*. On smooth landscapes (low K) it is maximised by
+    wide breadth; as ruggedness rises the peak shifts to an INTERMEDIATE breadth (full promiscuity
+    prematurely converges below it) — the mating-system image of E9's "optimal recombination rate
+    shrinks with ruggedness".
+(B) mean fitness / global optimum — the *typical* individual. Monotonically favoured by breadth at
+    every K: panmixia lifts the whole population toward a good consensus.
+(C) diversity (mean normalised pairwise Hamming) — monotonically DESTROYED by breadth at every K
+    (promiscuity homogenises), the reservoir largest under monogamy and on rugged landscapes.
+
+The tension between (A)/(C) is the result: promiscuity maximises the typical model and kills diversity;
+on rugged landscapes the best model needs preserved diversity, so an intermediate breadth wins.
+
+Usage:  python figures/plot_E14.py
+"""
+
+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, savefig  # noqa: E402
+
+
+def main() -> None:
+    df, _ = load_bundle("results/E14")
+    last = df[df["generation"] == df["generation"].max()].copy()
+    last["best_n"] = last["best_fitness"] / last["global_opt"]
+    last["mean_n"] = last["mean_fitness"] / last["global_opt"]
+    Ks = sorted(last["K"].unique())
+    cmap = plt.get_cmap("viridis")
+    colors = {K: cmap(i / max(1, len(Ks) - 1)) for i, K in enumerate(Ks)}
+
+    fig, axes = plt.subplots(1, 3, figsize=(16, 5))
+    panels = [
+        ("best_n", "best fitness / global optimum", "(A) the champion: best model in the population",
+         "best fitness peaks at INTERMEDIATE breadth\non rugged landscapes (the peak shifts left as K rises)"),
+        ("mean_n", "mean fitness / global optimum", "(B) the typical model: population mean",
+         "monotonically favoured by wide breadth\n(panmixia lifts the whole population)"),
+        ("diversity", "diversity (mean pairwise Hamming)", "(C) standing diversity",
+         "monotonically destroyed by breadth\n(promiscuity homogenises; monogamy preserves)"),
+    ]
+    for ax, (col, ylab, title, subtitle) in zip(axes, panels):
+        for K in Ks:
+            g = (last[last["K"] == K].groupby("breadth")[col]
+                 .agg(["mean", "sem"]).reset_index())
+            ax.errorbar(g["breadth"], g["mean"], yerr=1.96 * g["sem"].fillna(0.0),
+                        marker="o", lw=1.8, capsize=2, color=colors[K], label=f"K={K}")
+        ax.set_xscale("log")
+        ax.set(xlabel="mate-pool breadth  (monogamous ← → promiscuous)", ylabel=ylab)
+        ax.set_title(f"{title}\n{subtitle}", fontsize=9)
+        ax.legend(title="ruggedness", frameon=False, fontsize=8)
+
+    fig.suptitle("E14 — monogamy vs promiscuity: the best mate-pool breadth shrinks as skills get more entangled",
+                 y=1.02, fontsize=13)
+    fig.tight_layout()
+    savefig(fig, "results/E14", "E14")
+
+
+if __name__ == "__main__":
+    main()