Giorgio Gilestro
0f7b775ae5
The culmination. A finite population of agents (genotypes, L loci) evolves
on a rugged NK landscape that IS reality (knowledge/dynamic_society.py),
composing the four operators the whole study built toward: grounding,
directed recombination (sex), quality-diversity selection, and mutation.
Grounding is made load-bearing via the consensus-conformity (self-
consumption) mechanism (GG decision): selection acts on
g*true_fitness + (1-g)*conformity, where conformity = agreement with the
population's own consensus, so at g=0 the society optimises fitting-the-
crowd rather than reality.
4-arm ablation (12 reps), each breaking distinctly, only the full society
climbing (global_opt ~ 0.79):
- full 0.78 climbs to the optimum, diversity maintained longest
- no_sex 0.77 can't recombine to escape local optima
- no_diversity 0.74 greedy: collapses diversity fastest, worse local optimum
- no_grounding 0.48 self-consumption collapse to an unfit consensus
(trains on the crowd -> confident-but-wrong mean;
conformity-true gap ~ 0.5)
This integrates E1-E6 + the learning kernel + E7-E10 into one system and
shows the Lamarckian society needs ALL of grounding + directed sex +
diversity: on a rugged landscape you need diversity to explore basins, sex
to recombine them, and grounding to select on reality -- remove any one and
you fail differently. Closes the C3 vertical claim analytically; the LLM
rung remains the eventual empirical instantiation.
New: knowledge/dynamic_society.py, configs/layer1/E11.yaml, figures/
plot_E11.py, README, tests/test_dynamic_society.py (+5). kind:
dynamic_society dispatch; make layer1 wired. 122 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
37 lines
1.8 KiB
Makefile
37 lines
1.8 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
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# reproducibility source of truth; every target runs inside it via `uv run`.
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.PHONY: env env-neural env-mnist test layer1 layer2 neural mnist figures clean
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env: ## build .venv from the committed lockfile
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uv sync --extra dev
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env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
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uv sync --extra dev --extra neural
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env-mnist: ## add torchvision for the real-MNIST confirmation tier
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uv sync --extra dev --extra neural --extra mnist
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test: ## correctness tests + scientific-validation tests (the spine of trust)
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uv run pytest
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layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
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for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the heavy MNIST tier)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
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for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
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*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
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clean: ## remove caches and generated results (keeps committed manifests)
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rm -rf .pytest_cache **/__pycache__
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find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
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