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>

paper/the-evolution-of-sex-for-ai-accessible.md

@@ -187,6 +187,18 @@ only worked as a whole. Biologists call this **outbreeding depression**, and we
 gets. The design rule: *combine freely when skills are independent; combine sparingly and carefully when
 they're tangled.*
 
+**How *widely* you mate matters too.** That last point was about *how much* to mix; a separate knob is
+*who mixes with whom*. **Monogamy** = each model only ever combines within a small, fixed circle;
+**promiscuity** = any model can combine with any other. Almost all model-merging today is promiscuous by
+default — throw everything in one pot. But there's a catch: wide mixing spreads good traits fast, but it
+also makes the whole population converge to the *same thing*, killing variety. Narrow, local mixing keeps
+separate sub-groups exploring different solutions. We tested this against tangledness, and the best answer
+*moves*: on simple (independent-skill) problems, wide promiscuous merging is best; but the more tangled
+the skills, the more you want to *narrow* it — full promiscuity converges too fast onto one mediocre
+solution and finds a *worse* champion, while keeping structured sub-groups preserves the variety a hard
+problem needs. So the rule extends: *merge widely for independent skills; keep separate sub-populations
+("island" merging) for tangled ones.*
+
 **AI can do sex better than biology can.** Biology is stuck with two parents, mating more or less at
 random, and can't inspect a child before it's born. AI has none of those limits. It can combine **many**
 parents at once; it can **choose** which parents to combine, for complementary skills; and it can
@@ -449,6 +461,12 @@ real language models. Here's the shape of the evidence (a separate document has
   level off below. On *tangled* problems, blind combining instead produces below-parent children
   (outbreeding depression) — and *directed* combining (choose mates, screen offspring, many parents)
   reliably fixes it.
+- **Monogamy vs promiscuity.** Sweeping how *widely* models merge — from local/monogamous to
+  everyone-with-everyone/promiscuous — against how tangled the skills are, the best breadth **shrinks as
+  the skills get more tangled**: wide promiscuous merging wins when skills are independent, but on tangled
+  problems it converges too fast onto one mediocre solution and finds a worse champion, so keeping
+  structured sub-populations wins. (Promiscuity always lifts the *typical* model but always destroys
+  variety.) A merging design knob the field, which throws everything in one pot, doesn't currently have.
 - **The combining claims, in real language models — with a sharp condition.** Merging fine-tuned Qwen
   models (up to 7B on a GPU cluster) produces a generalist that beats every specialist parent; and
   keeping parents separate and *routing*, or *breeding and screening* offspring, beats the plain average

paper/the-evolution-of-sex-for-ai.md

@@ -270,7 +270,7 @@ beats the average in exact proportion to how far the average is from the best at
 that sharpens rather than weakens the claim, and that a practitioner needs before spending compute on
 the fancier operator.
 
-Two caveats keep this honest, and both are results, not hand-waving.
+Three results keep this honest, and all are results, not hand-waving.
 
 *Sex can backfire.* When the parents' skills are not cleanly separable but **entangled** — when the
 value of one capability depends on which others are present (geneticists call this **epistasis**) —
@@ -280,6 +280,22 @@ reproduce it: on "rugged" (highly entangled) problems, naive merging drops offsp
 parents, and the more you mix the worse it gets. The design rule that falls out is simple: *merge
 freely when skills are complementary; merge sparingly, and carefully, when they are entangled.*
 
+*The mating system matters too — not just who mates, but how widely.* The result above is about the
+recombination *rate*; a separate knob is the population's **mating structure** — whether reproduction is
+**monogamous** (each model recombines within a narrow, local circle) or **promiscuous** (mates drawn
+freely from the whole population). Almost all model-merging implicitly assumes promiscuity — fuse
+everything, or route over one flat pool — but population genetics says the breadth of gene flow is itself
+consequential, because wide flow spreads good variants fast while **homogenising** the population, and
+narrow flow preserves the distinct sub-populations needed to explore several solutions at once (Wright's
+*shifting balance*). We sweep exactly this breadth against landscape ruggedness, and the optimum moves:
+on smooth (additive) landscapes wide, promiscuous mating is best (spread the one good direction fastest),
+but as the landscape gets rugged the best breadth **shrinks to an intermediate value** — full promiscuity
+prematurely converges onto one basin and finds a *worse* champion, while pure monogamy over-fragments.
+Throughout, wide mating lifts the *typical* model but monotonically **destroys diversity** — so on rugged
+problems, where the best model needs preserved diversity to be found, structured (partly monogamous)
+merging wins. The design rule extends the one above: *merge widely when skills are additive; keep
+structured sub-populations — island-style merging — when skills are rugged.* (Figure: `results/E14/E14.png`.)
+
 *AI can do sex better than biology can.* Biology is stuck with two parents, mating roughly at random,
 and cannot inspect an offspring before it is born. An AI has none of those limits. It can recombine
 **many** parents at once; it can **choose** which parents to combine, for complementarity; and it can
@@ -599,6 +615,13 @@ shape.)
   produces below-parent offspring (outbreeding depression) — and *directed* recombination (choose mates,
   screen offspring, unbounded parents) reliably fixes it. This is the concrete evidence for the paper's
   central reframing.
+- *The mating system, not just the mating.* Sweeping how *widely* models recombine — from monogamous
+  (local, structured) to promiscuous (panmictic) — against landscape ruggedness, the best breadth
+  **shrinks as skills get more entangled**: wide, promiscuous merging wins on additive landscapes, but on
+  rugged ones it prematurely converges to a worse champion and an intermediate, structured breadth wins,
+  because promiscuity monotonically destroys the diversity a rugged search needs. A merging-native design
+  axis — *merge widely for additive skills, keep island-structured sub-populations for entangled ones* —
+  that the model-merging literature, which assumes panmixia, does not have.
 - *The recombination claims, in real language models — with a sharp condition.* Merging LoRA-specialised
   Qwen models (up to 7B on a GPU cluster) produces a generalist that beats every specialist parent
   (Fisher–Muller, for real); and keeping parents intact and *routing*, or *breeding and screening*