assembly-economics/models/Calibration.md

Calibration of GADM-0.4 to measured data

Status: v0.5 (2026-10-13). ASSEMBLY-WP-0013, then the 2026-10-13 review (§ 3.1): 36 of 40 targets measured, all 40 inside their ranges after fitting. v0.5 adds Argentina's ecological headroom (global planetary-boundary measure), which fits AR's ereg (0.0058) instead of the default. Calibrates starting states for US, EU, DE and AR. Argentina has no measured adoption growth, so it is calibrated only on priors. Most state targets are literature priors that the next review must verify against checkpoint data.

Reproduce:

cd simulations/gadm02
python3 -B calibrate_data.py        # results/calibration.md, results/calibrated_starts.json
python3 -B calibrated_readings.py   # results/calibrated_readings.md

1. Targets and observation operators (T01)

simulations/gadm02/calibration_targets.json holds, per jurisdiction and model quantity, a target range in normalized model units, its basis (measured at checkpoint 0, or prior from literature up to 2025) and the observation operator: how the raw indicator maps onto the model quantity.

Model quantity	Operator	Basis
K_Ω level	firm-use share (L1, L2) × usage intensity; a few percent of work hours AI-assisted	prior (level), measured (firm share)
K_Ω one-year ratio	ratio of the firm-use share over the last year	measured: US 1.46, EU 1.48, DE 1.31
Pd, Pi, Pmem, Plin, Pm	leading search share; top-3 cloud share; OS/app-store duopoly; top-3 frontier developers; digital-sector concentration	prior
B	labour share plus broad transfers; trend from the F1 index	prior (level), measured (US trend)
A	self-directed leisure share (WP-0008 grounding)	prior
E	planetary-boundary position	prior
Σ	share more concerned than excited (Pew), Eurobarometer	prior

Two operators carry most of the uncertainty. K_Ω level: firm use is not task coverage, so the level is a wide range (0.05–0.20 for the US) and the measured growth does the work. Pmem: whether OS and app-store control is the "membrane" of GADM is a modeling judgment. The range is wide (0.40–0.85).

2. Routine (T02)

calibrate_data.py, standard library only:

1. Set stocks with targets (power channels, E, Σ) and fit the parameters that put them at rest: channel counters (gadm04.calibrate), ecological regeneration ereg (dE = 0), base labour share wL0 (B on target). The nearest archetype's levers are applied, except permissiveness.
2. Relax wealth, cohort attention and cohort agency to equilibrium.
3. Adoption: scale B2's adoption parameters by k. For each k, find the model time t* where K_Ω's one-year ratio equals the observed ratio. Choose k so that K_Ω(t*) is closest to the level target (log grid, k ∈ [0.25, 4]).
4. The calibrated start is the full state at t* with the fitted parameters.

Identifiability. The channel levels, E, Σ and B are set, not tested. They fit by construction, and only their drift until t* is informative. The adoption block is the only real fit: two measured or semi-measured quantities (level, growth) against two free values (k, t*). Everything else in the model (H, X, Q, N, S, F, G) remains at uninformed defaults.

3. Results (T03)

Jurisdiction	Levers	k (× B2 speed)	t*	K_Ω(t*)	wL0	out of range
US	J3	1.83	1.2 y	0.120	0.530	A −1.1 (attention extraction of J3), B trend −1.4 (falls slightly faster than measured)
EU	J1	1.41	0.9 y	0.093	0.637	none (A at the upper edge)
DE	J1	1.30	1.5 y	0.112	0.637	none
AR	J2	—	—	—	0.387	no adoption data

Data verdict on the band anchors. Aligned by growth alone, with no k:

• B1 cannot produce the observed growth at all, in any of the three jurisdictions.
• B2 lands slightly low (z −0.5 to −0.9).
• B3 fits (z −0.1 to +1.1).
• B4 overshoots the level (z +1.4 US, +3.1 EU, +4.2 DE).

The present is between B2 and B3. B1 is ruled out as a description of today, and B4 as a description of today's level. B4 can still describe the future if self-enhancement starts later.

3.1 Calibration v0.4: measured targets and fitted parameters

The review checkpoints/2026/2026-10-13-review.md measured 35 of the 39 targets:

• adoption level and growth (BTOS, Eurostat; AR level from the Google/IDB study);
• search share (Pd), mobile-OS HHI (Pmem), cloud top-3 share (Pi), frontier-model top-3 share (Plin);
• claims breadth B (compensation plus social benefits, % of GDP) and the US B trend;
• attention: US (ATUS 2025), DE and EU proxy (Destatis time-use survey 2022);
• ecological headroom E (Planetary Health Check 2025: 7 of 9 boundaries breached) and US market concentration Pm (top-7 share of S&P 500 value);
• sentiment Σ for US (Pew 2026), EU (Eurobarometer) and AR (La Nación Data).

Fitted parameters per jurisdiction:

	k (× B2)	t*	wL0	ereg	aext	auto
US	2.00	1.2 y	0.478	0.0062	0.387	0.26 (fitted to the B trend)
EU	1.54	0.9 y	0.523	0.0063	0.719	0.60 (default)
DE	1.41	1.5 y	0.616	0.0066	0.702	0.60
AR	2.00	1.1 y	0.387	0.0058	0.580	0.60

Fit: 39 of 39 targets inside their range, including 35 of 35 measured targets.

• Fitted against data: adoption speed k and alignment t* (level and growth), wL0 (B level), aext (attention, US measured, others prior) and auto (US B trend).
• Set by assignment: the channel levels are set to the measured values, and the channel counter coefficients are fitted so that those levels are at rest. That is a calibration of the counters, not a test of them.

Findings:

1. Adoption. The anchor verdict holds. B1 cannot produce the observed growth anywhere, and B3 fits best (US z 0.0, AR 0.0, EU +0.5).

2. Automation. The measured US labour-share decline is slower than the default automation strength implies. Fitted auto is 0.26 instead of 0.6.

3. Domination starts at or above the floor: US 0.68, EU 0.72, AR 0.75 (floor 0.70). The large contributions are measured: frontier-model spend is 88 % with three firms, and search is 85–94 % with one.

4. Ecology is close to its floor. Measured E is 0.235 against a floor of 0.20. Ecological regeneration becomes one of the leading sensitivity drivers (§ 4.1).

Still priors: market concentration Pm for the EU and AR, and AR adoption growth (only one level point). They stay in ASSEMBLY-WP-0015-T06.

4. Readings from the calibrated start (T04)

calibrated_readings.py applies each archetype's levers from the calibrated present, with the legitimacy loop off and on.

Reading (uncalibrated source)	US	EU	AR	Status
R1 rights-first levers give the best VFAC (§ 4.3 of AdoptionBand)	holds	holds	holds	survives
R2 the legitimacy loop damps adoption (§ 3.3)	fails under J1	holds	fails under J1	revised
R3 lagging alone costs early output (§ 4.3)	−0.074 Ȳ	−0.071	−0.121	survives, stronger
R4 the current regime breaks the domination floor by t 100	yes (J3; P 0.68 → 0.82)	yes (J1; 0.72 → 0.82)	yes (J2; 0.75 → 0.86)	new

1. R2 revised: the loop's sign depends on the regime. Under rights-first levers, domination falls, perceived benefit rises, and sentiment accelerates adoption (US: K_Ω peak 0.21 → 0.26). Under extractive regimes it brakes adoption as before. Damping is not a property of the loop. It is a property of the loop in a regime where adoption raises domination.
2. R4: today's domination is at the floor. Calibrated to measured concentration (v0.3), aggregate P starts at 0.69–0.75 against a floor of 0.70. Under the current regime, continued adoption pushes it over the floor in all three jurisdictions. Uncalibrated GADM started at P = 0.37 and understated this.
3. The EU has no lever headroom left within J1. The channel counters are calibrated so that the current regime is at rest. Lever effects are therefore relative to the current regime. The US gains by moving from J3 to J1 levers (P 0.68 → 0.54). The EU is already at J1 settings, and its P rises to 0.82. Within this closed-economy model, the EU's domination comes from levels set abroad (lineages, platforms), which its levers do not reach. This is the strongest argument yet for WP-0011.
4. Readings that still need re-running: the GSA per anchor (WP-0009-T05) was computed around the uncalibrated anchors. The prediction ranges (P-2026-001 … 003) used growth alignment without k. They are consistent with § 3, because B2–B3 bracket the fitted speed.

4.1 Global sensitivity around the calibrated start (WP-0015-T04)

gsa.py --model gadm04 --start US|EU (results/gsa_gadm04_start_US.md, _EU.md): Morris and Sobol analysis of final VFAC around calibration v0.4. The scenario's policy levers are applied; adoption and automation stay at their fitted values.

Scenario	US: top 3 by ST	EU: top 3 by ST
Baseline	dO 0.29, IX 0.26, ell 0.08	IX 0.33, dO 0.13, lX 0.11
Generative regime	IX 0.20, ereg 0.11, dX 0.10 (expl 0.09, rank 5)	IX 0.24, lX 0.13, ereg 0.10

1. The commons block leads from the calibrated start. Commons investment IX, enclosure loss lX and decay dX rank at the top in both jurisdictions and both scenarios.
2. Ecology becomes a leading driver. With E measured near its floor, ecological regeneration ereg ranks second or third in the generative regime. It did not appear in the uncalibrated top five.
3. The governance reading of WP-0009-T05 does not survive calibration unchanged. Around the uncalibrated B3–B4 anchors, explainability expl led the generative regime. From the calibrated start it is fifth (US) or lower (EU). With domination already high and the counters calibrated to hold it, the commons and ecology parameters matter more for VFAC than the epistemic lever.
4. Organism depreciation dO still matters most for the US baseline (ST 0.29), less for the EU (0.13), where adoption is slower and smaller.

5. Re-calibration at checkpoints (T05)

When a checkpoint brings new data:

1. Replace prior targets with measured ones where an indicator now has data, and record the change (date, source) in the JSON op text.
2. Re-run calibrate_data.py and calibrated_readings.py. Commit the results with the checkpoint.
3. Compare k, t* and the anchor verdict with the previous calibration. A change of verdict (for example B3 → B4 fitting best) is a flip candidate for WP-0012.
4. Predictions are not edited. New predictions from the re-calibrated start are added as new register entries.

6. Limitations

• Four targets are still priors (§ 3.1). Several measured values come from secondary reports (see the review record).
• One adoption speed parameter (k) for four parameters (aO, ell, oseed, selfenh). k does not separate investment from learning.
• The channel-counter calibration makes the current regime a rest point by construction. A jurisdiction that is in fact far from rest (for example concentration still rising for reasons outside the model) is misread as stable.
• Closed economy (WP-0011).