Giorgio Gilestro
3b9f4f7893
One self-contained README.md per results/ figure folder (Layer 1 E1-E6 and Layer 1.5 bridge/collapse/grounding/architectures/recombination): plain-language claim, setup, a compact symbol glossary, a panel-by-panel walkthrough, and the takeaway + falsifier. Auto-renders when browsing the folder; carries the honest caveats (grounding's ruler reframing, the excluded VAE). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
32 lines
2.1 KiB
Markdown
32 lines
2.1 KiB
Markdown
# E5 — Quality-diversity selection preserves diversity where greedy selection destroys it
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**Claim tested:** if each generation you *select* which outputs to keep, does chasing the "best"
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outputs (greedy) accelerate collapse — and does rewarding novelty instead prevent it?
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**Setup (Layer 1, pure math).** `K = 500` items, Zipf truth, `n = 200`, 400 generations, 100 repeats,
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all arms given the same grounding. Three selection modes: **none** (grounding only, no selection),
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**greedy** (keep the fittest — highest-`p*` — items), and **quality-diversity (QD)** (a novelty
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bonus `w_i ∝ f_i · p_i^{-α}` that up-weights rare items). The novelty exponent `α` is swept over
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`{0.5, 1, 2}`.
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### Symbols
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- **greedy** — select toward the fittest/most-probable items (directional pressure).
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- **QD (quality-diversity)** — select for fitness *and* novelty; `α` = strength of the novelty bonus.
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- **`H`** diversity; **support** = number of distinct items surviving.
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### The three panels
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1. **Diversity trajectories.** `H` over generations: red = greedy (crashes toward ~0, i.e. fixation
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on a few items); orange/blue = QD at `α = 1, 2` (holds a high plateau); green = none (reference).
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Greedy selection is a *second* collapse engine on top of drift.
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2. **Novelty dose–response.** Stationary `H` vs the novelty exponent `α` for QD (orange dots), with
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greedy (red dashed) and none (green dashed) as reference lines. QD sits above greedy for **every**
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`α`, and rises as the novelty bonus strengthens.
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3. **Surviving items per arm.** Stationary support (number of distinct items alive) as bars: greedy is
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lowest; QD arms keep progressively more items alive as `α` grows; none is the reference.
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### Takeaway
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Optimising only for "what looks best" (greedy) collapses the population onto a handful of winners; a
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novelty-rewarding, quality-diversity objective actively **re-introduces and holds the tail**. Key
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numbers: greedy `H ≈ 0.01` (near-total fixation) vs QD `H ≈ 0.48–0.88` rising with `α`.
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**Falsifier (not triggered):** if QD's stationary `H` had been ≤ greedy's, quality-diversity would be
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doing no work.
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