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>

results/E14/README.md

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+# E14 — Mating systems: monogamy vs promiscuity (mate-pool breadth)
+
+**Claim tested.** The society experiments (E8–E11) assumed **panmixia** — every offspring recombined
+from parents sampled across the whole population. Biology's mating systems instead span a continuum
+from **monogamy** (mating within a narrow, local circle) to **promiscuity** (mates drawn freely from
+everyone), and population genetics says the choice matters: wide gene flow spreads a good allele fast
+but **homogenises** the population, while restricted gene flow (population structure / *isolation by
+distance*) keeps demes distinct so several fitness peaks can be explored in parallel (Wright's shifting
+balance). E14 asks how the best mating system depends on how **entangled** the skills are.
+
+**Setup.** A finite population of `N=48` genotypes (`L=12` biallelic loci) evolves on a Kauffman **NK**
+landscape (ruggedness `K`). Agents sit on a **ring**; an offspring's second parent is drawn from a
+window of half-width `≈ breadth·N/2` around the focal parent, so **mate-pool breadth** `b` is a single
+scalar: `b→0` = monogamous / structured (local mating), `b=1` = promiscuous / panmictic. Selection is
+**local** — an offspring replaces the incumbent at its own ring position only if strictly fitter — so
+restricted mating can actually sustain distinct demes instead of being washed out. Sweep `b ∈ {0.03,
+0.08, 0.17, 0.35, 0.6, 1.0}` × `K ∈ {0, 3, 6, 10}`, 60 generations, 20 replicates, `μ=0.003`,
+crossover rate 0.5. Bitwise-reproducible from the master seed.
+
+### Results — the best breadth shrinks as the landscape gets more rugged
+`best_fitness / global_opt` (the *champion*), mean over 20 reps; **bold = best breadth at that K**:
+
+| K \ breadth | 0.03 | 0.08 | 0.17 | 0.35 | 0.60 | 1.00 |
+|---|---|---|---|---|---|---|
+| **0** (additive) | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
+| **3** (mild) | 0.995 | 0.993 | 0.997 | 0.993 | **0.9997** | 0.993 |
+| **6** (rugged) | 0.986 | 0.972 | 0.983 | **0.989** | 0.984 | 0.982 |
+| **10** (very rugged) | 0.961 | 0.968 | 0.967 | **0.980** | 0.964 | 0.965 |
+
+- **K=0** saturates: an additive (single-peak) landscape is solved by everyone regardless of mating, so
+  the champion metric can't discriminate (it only shows up in diversity, below).
+- **K=3**: the optimum is at **wide** breadth (`b=0.6`) — near-promiscuous mating maximises the champion
+  when the landscape is mild.
+- **K=6, K=10**: the optimum moves to an **intermediate** breadth (`b=0.35`), and *full promiscuity*
+  falls below it. Wide mating **prematurely converges** on rugged landscapes; pure monogamy
+  over-fragments (too little gene flow to combine complementary basins). The best of both is
+  intermediate structure — the mating-system image of E9's "optimal recombination rate shrinks with
+  ruggedness."
+
+### Results — the diversity/mean tension that drives it
+Two monotone effects, opposite in sign, at **every** K (mean over reps at K=10):
+
+| breadth | 0.03 | 0.08 | 0.17 | 0.35 | 0.60 | 1.00 |
+|---|---|---|---|---|---|---|
+| mean fitness / opt | 0.890 | 0.914 | 0.926 | 0.941 | 0.941 | 0.943 |
+| diversity (pairwise Hamming) | 0.441 | 0.413 | 0.384 | 0.346 | 0.265 | 0.282 |
+| distinct local optima occupied | 11.0 | 8.6 | 7.4 | 7.3 | 7.2 | 6.9 |
+
+- **Mean fitness** rises monotonically with breadth: panmixia lifts the *typical* individual toward a
+  good consensus fastest.
+- **Diversity** and **occupied peaks** fall monotonically with breadth: promiscuity **homogenises**;
+  monogamy preserves the standing variation (and the parallel exploration of distinct basins) — most
+  strongly on rugged landscapes.
+
+So promiscuity maximises the *typical* model and destroys diversity; on a rugged landscape the *best*
+model needs that preserved diversity, so an intermediate breadth wins the champion even though the wide
+breadth still wins the mean. (Panel A = champion, Panel B = mean, Panel C = diversity.)
+
+### Positioning
+This is the population-**structure** axis the model-merging literature does not have. Merging/soup work
+implicitly assumes panmixia (fuse everything, or route among a flat pool); E14 says the *breadth* of who
+merges with whom is itself a design knob, and its optimum is set by the entanglement of the skills:
+**merge widely when skills are additive; keep sub-populations (structured / island merging) when skills
+are rugged and diversity must be preserved to explore and later combine basins.** It complements E9
+(recombination *rate*) and E11 (diversity is load-bearing) on a new, orthogonal axis. **Falsifier (not
+triggered):** the best breadth independent of `K` (no crossover), or promiscuity best at every
+ruggedness — instead the optimal breadth shifts from `0.6` (K=3) to `0.35` (K≥6), and diversity is
+monotonically lost to breadth throughout.

results/E14/manifest.json

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+{
+  "experiment": "E14",
+  "master_seed": 20260709,
+  "git_commit": "9fea375ff89f2b88a4deb5881894087555558863",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 29280,
+  "results_sha256": "5766302157f44c0f27228f361b43c06b0651697a7dcc38848eb1d94d0502faa6"
+}

results/E14/resolved_config.yaml

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+experiment: E14
+seed: 20260709
+n_replicates: 20
+source_config:
+  experiment: E14
+  kind: mating_system
+  seed: 20260709
+  n_replicates: 20
+  mating:
+    L: 12
+    N: 48
+    breadth: 1.0
+    K: 0
+    recomb_rate: 0.5
+    mu: 0.003
+  generations: 60
+  sweep:
+  - param: mating.K
+    values:
+    - 0
+    - 3
+    - 6
+    - 10
+  - param: mating.breadth
+    values:
+    - 0.03
+    - 0.08
+    - 0.17
+    - 0.35
+    - 0.6
+    - 1.0
+  output:
+    dir: results/E14