society: make the sexual-transmission model rigorous (E9 epistasis, E10 directed sex)
Deepen the sexual-reproduction frame before entering the full society, on
the two facets GG chose: landscape robustness and directed recombination.
Adds a Kauffman NK landscape (genotype.nk_fitness, tunable ruggedness),
finite n-parent crossover (genotype.crossover, per-gap recombination rate),
and hill-climb (parents = local optima = trained models).
E9 (recomb_landscape) -- the "why sex?" test: E8's dramatic super-parent
result used an ADDITIVE landscape. On rugged/epistatic landscapes, blindly
recombining local optima causes OUTBREEDING DEPRESSION -- offspring fall
below the parents, worse with both ruggedness and recombination rate (K=8,
free recomb: ~ -0.23), and the optimal recombination rate shrinks as
ruggedness grows. Design rule: merge freely when skills are complementary/
additive; sparingly (and with selection) when entangled.
E10 (directed_sex) -- directed sex beats biological sex: biology is stuck
with 2 random-mating parents and no offspring preview; an AI can choose
complementary mates, evaluate many recombinant offspring, keep the fittest,
and use unbounded parents (iterated recombine-then-select). Random
("biological") sex craters with ruggedness (0.66->0.51); directed sex
tracks/exceeds the best parent at every ruggedness -- converting the
outbreeding-depression catastrophe into a win. No biological analog.
Complete sexual-transmission picture: dramatic super-parent offspring when
skills are complementary (E8); outbreeding-depression risk when entangled
(E9); directed sex resolves the risk (E10). configs/layer1/{E9,E10}.yaml,
figures/plot_{E9,E10}.py, READMEs, +5 tests (117 green).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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results/E10/E10.pdf
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results/E10/README.md
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# E10 — directed sex beats biological sex (the distinctly-AI superpower)
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**Claim tested.** E9 showed that on rugged (epistatic) landscapes, blind recombination causes
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outbreeding depression — offspring worse than parents. Biology is largely stuck with this: two
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random-mating parents, no way to preview offspring. But an **AI is not** — it can choose mates,
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evaluate many recombinant offspring, and keep only the fittest, over rounds, with no two-parent
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limit. Does this **directed sex** rescue recombination where blind sex fails?
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**Setup.** Parents are local optima of a Kauffman NK landscape (`L=12`), swept over ruggedness `K`.
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Three strategies compared, all as deployed capability (fitness ∈ [0,1]): **best single parent**;
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**random sex** (blind mating, free recombination, no offspring selection — biology's default);
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**directed sex** (iterated recombine-then-select-offspring, `rate=0.2`, 5 rounds — the AI move).
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24 replicate landscapes.
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### Symbols
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- **random sex** — blind: random parents, free recombination, take the offspring as they come.
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- **directed sex** — choose complementary mates + generate many offspring + keep the fittest + repeat; unbounded parents.
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- **global optimum** — the landscape's best genotype (the ceiling).
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### The two panels
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1. **Capability vs ruggedness.** As `K` grows, **random sex (blue) craters** (0.66 → 0.51 — deep
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outbreeding depression), while **directed sex (red) tracks the best parent and the global optimum**,
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staying near the ceiling at every ruggedness.
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2. **Edge over the best parent.** Directed sex stays **≥ 0** (at or above the parents) across all `K`;
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random sex falls to **≈ −0.2** (far below). Directed sex converts a catastrophe into a win.
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### Takeaway
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The move biology cannot make — **choose your mates, evaluate your offspring before you commit, and
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recombine as many parents as you like** — is exactly what makes AI sexual reproduction robust. Blind
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merging of entangled models is dangerous; *directed* merging (generate many candidate merges, keep
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the best) is safe and can exceed every parent. This is the practical payoff of the sexual-transmission
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model and the distinctly-AI superpower with no biological analog. **Falsifier (not triggered):** if
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directed sex did no better than random sex, or never recovered the best-parent level on rugged
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landscapes, the "AI beats biology" claim would fail.
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results/E10/manifest.json
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results/E10/manifest.json
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{
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"experiment": "E10",
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"master_seed": 20260705,
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"git_commit": "62c68d6c8c4090e62cf9ee17ac7b7d1eff7a6955",
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"python": "3.14.5",
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"libraries": {
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"numpy": "2.5.0",
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"scipy": "1.18.0",
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"pandas": "3.0.3",
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"pyarrow": "24.0.0"
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},
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"rows": 120,
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"results_sha256": "b2d53d0a3949be0b67e3b8dffec56121d54e20c593e6302717a73a8a132f17cb"
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}
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results/E10/resolved_config.yaml
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results/E10/resolved_config.yaml
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experiment: E10
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seed: 20260705
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n_replicates: 24
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source_config:
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experiment: E10
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kind: directed_sex
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seed: 20260705
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n_replicates: 24
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society:
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L: 12
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n_parents: 6
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pop: 200
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keep: 8
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rounds: 5
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rate: 0.2
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sweep:
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- param: K
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values:
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- 2
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- 4
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- 6
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- 8
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- 10
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output:
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dir: results/E10
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