tegwick
25f91c73c7
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assistant: claude-code Assistant-Model: opus Assistant-Process: 14913@bnt-lap001 Assistant-Session: add7ef69-b901-4756-9a67-34cf1125d96f
137 lines
7.1 KiB
Markdown
137 lines
7.1 KiB
Markdown
# Calibration of GADM-0.4 to measured data
|
||
|
||
**Status:** v0.1 (2026-10-13), ASSEMBLY-WP-0013. 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:
|
||
|
||
```bash
|
||
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.
|
||
|
||
## 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) | −0.080 Ȳ | −0.078 | −0.068 | **survives, stronger** |
|
||
| R4 the current regime breaks the domination floor by t 100 | yes (J3) | yes (J1) | yes (J2) | **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 close to the floor.** Calibrated to observed
|
||
concentration, aggregate P starts at 0.59–0.65 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 0.64 → 0.54). The EU is already at J1 settings, and its P
|
||
rises to 0.77. 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.
|
||
|
||
## 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
|
||
|
||
- Most state targets are priors, not measurements. Verifying them is
|
||
WP-0013-T06.
|
||
- 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).
|