assembly-economics/simulations/README.md
tegwick 218d196738 ASSEMBLY-WP-0008: empirical grounding of key GADM parameters
- Research on organism adoption, commons, power persistence, lobbying,
  human-capital persistence, age gaps and care load
- grounding.py: grounded parameter sets (central / slow adoption) with own
  equilibrium start and recalibrated channels; tested
- grounded_readings.py: all structural readings survive grounding; default
  adopts organisms too fast; governance stakes rise with adoption speed;
  care load overstated ~4x (joint recalibration of attention extraction)
- ParameterGrounding.md v0.2: register with statuses, evidence, mappings,
  validation/falsification tests for ASSEMBLY-WP-0007
- GADM-0.4 model version finalised

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Assistant: claude-code
Assistant-Model: opus
Assistant-Process: 1494246@bnt-lap001
Assistant-Session: caf6de6f-f9e4-47ce-a584-49baa271fa4b
2026-10-12 09:45:19 +02:00

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Markdown

# Simulations
Runnable implementations of the models in `models/`. Each simulation names the
model version it implements.
| Directory | Model | Entry points |
|-----------|-------|--------------|
| `gadm02/` | GADM-0.2 (`models/GADM-0.2.md`) | `run_scenarios.py`, `sensitivity.py`, `gsa.py`, `test_gadm02.py` |
| `gadm02/gadm03.py` | GADM-0.3: power channels, wealth, attention (`models/GADM-0.3.md`) | `power_channels.py`, `wealth.py`, `organism_grid.py`, `test_gadm03.py` |
| `gadm02/gadm03c.py` | GADM-0.3 with life-cycle cohorts (`models/GADM-0.3.md` § 5) | `life_cycle.py`, `test_gadm03c.py` |
| `gadm02/gadm04.py` | GADM-0.4: durable capability, grounded dO, equilibrium start, cohort agency (`models/GADM-0.4.md`) | `life_cycle.py --model gadm04`, `robustness.py`, `agency.py`, `grounded_readings.py`, `test_gadm04.py` |
| `gadm02/grounding.py` | evidence-grounded parameter sets for GADM-0.4 (`models/ParameterGrounding.md`) | `variant("central" \| "slow")` |
Python 3.10+, standard library only. Use `python3 -B` after quick edits: a stale
bytecode cache can mask same-size changes made within the same second.
```bash
python3 -m unittest discover -s simulations/gadm02 # tests
cd simulations/gadm02 && python3 run_scenarios.py # → results/summary.md + CSVs
cd simulations/gadm02 && python3 sensitivity.py # → results/sensitivity.md
cd simulations/gadm02 && python3 -B gsa.py --jobs 8 # → results/gsa.md (--model gadm04 → gsa_gadm04.md; --quick)
```
Results under `results/` are committed so that model documents can cite them.
Regenerate them after any change to the model or the scenarios.