assembly-economics/simulations
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
..
gadm02 ASSEMBLY-WP-0008: empirical grounding of key GADM parameters 2026-10-12 09:45:19 +02:00
README.md ASSEMBLY-WP-0008: empirical grounding of key GADM parameters 2026-10-12 09:45:19 +02:00

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.

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.