assembly-economics/models/GADM-0.4.md

Generative Assembly Dynamics Model — GADM-0.4

Status: GADM-0.4, exploratory. Complete for ASSEMBLY-WP-0006: durable human capability (§ 1, T01), grounded organism depreciation and robustness checks (§ 2, T02), global sensitivity analysis (§ 3, T03), equilibrium initialisation (§ 4, T04), agency per cohort (§ 5, T05). Sectors and goods were scoped into ASSEMBLY-WP-0007 (T06, GADM-Sectors-Scope.md).

GADM-0.4 builds on the GADM-0.3 cohort model (GADM-0.3.md § 5), which stays unchanged and reproducible in gadm03c.py. Time unit: one year.

---

1. Durable human capability (T01)

1.1 Motivation

GADM-0.3 failed hypothesis L3: an education push reached working-age capability after only about 9 years, and weakly. Working-age capability had no memory of childhood, because it relaxed to current conditions within about 8 years.

1.2 Structure

Each cohort's capability is split into:

Component	Formed by	Depreciation (per year)	Carried by ageing
durable D_c	education in childhood (hform, scaled by edu, plus commons X); small lifelong formation for adults (ll)	0.01 (children, working age), 0.02 (seniors), 0.04 (very old)	yes
current C_c	GADM-0.3 cohort dynamics (learning, mentoring, deskilling, care)	as GADM-0.3	yes

Effective cohort capability: H_c = \tfrac12 D_c + \tfrac12 C_c. Aggregate H, which the rest of the model uses, is population-weighted as in GADM-0.3.

Two couplings:

• Learning begets learning: a cohort's current-capability gains are scaled by 1 + lbl\,(D_c - 0.5).
• Birth dilution: newborns enter the children's cohort with no durable capability, so the cohort average is diluted at the birth rate (about 1/20 per year). A first draft omitted this term; durable capability then saturated near 0.85, leaving no room for an education effect.

Initial values: D_c = C_c = GADM-0.3 H_c, so effective capability starts identical to GADM-0.3 (unit-tested).

1.3 Results

Computed at T01, before dO was grounded (§ 2). The grounded values differ by at most 0.004.

	GADM-0.3 cohorts	GADM-0.4
Baseline VFAC (t = 100)	0.323	0.323
Generative regime VFAC	0.656	0.661
L3: education +50 % → final ΔH working age	+0.010	+0.032
L3: years to half of that effect	9	17 (durable part: 21)
L1: ageing + strong mentoring (tg 0.15), ΔH	−0.018	−0.029
L1: stable, no mentoring, ΔH	−0.043	−0.039

Full tables: simulations/gadm02/results/life_cycle_gadm04.md.

1.4 Readings

1. L3 is now supported. Education reaches working-age capability with a 17-year half-time, inside the hypothesised 15–20 years, and with three times the effect. The lag comes from cohort turnover (about 2.2 % per year) plus slow durable depreciation. The relaxation time is about 1/(0.01 + 0.022) ≈ 30 years.
2. L1 still holds, more weakly. Mentoring acts on current capability only, so its leverage on effective capability halves. An ageing society with strong mentoring still loses less than a stable society without it.
3. Macro results are almost unchanged. Durable capability adds memory, not level: baseline and generative VFAC move by at most 0.005. Policies on education matter mostly beyond a 20-year horizon, which strengthens the case for longer simulation horizons in cohort experiments.

1.5 Limitations

• The split ½ / ½ between durable and current capability is assumed.
• Children's current capability (inherited from GADM-0.3) still has no birth dilution. Only the durable component was corrected.
• hform was chosen so that baseline children's durable capability settles near 0.6. It has no empirical anchor yet (see T02).

---

2. Parameter grounding (T02)

Details and sources: ParameterGrounding.md.

• Organism depreciation is grounded: dO = 0.20 per year (evidence range 0.15–0.35, from software, R&D-capital and AI-hardware depreciation), instead of 0.06. Investment, learning and seeding are rescaled (×3.33) so the baseline organism path is unchanged. The evidence changes the timescale; the level of organism capability remains ungrounded.
• The key readings are robust to the grounded dO and to brackets on the ungrounded channel self-reinforcement (0.15–0.45) and wealth conversion (0.02–0.20). One exception at T02: P3's effect size grew to −0.078 at strong self-reinforcement. After T04 (§ 4) this exception disappears; it was partly an initial-condition artifact.
• With grounded dO: baseline VFAC 0.325, generative regime 0.665. The life-cycle results are essentially unchanged: L3 half-time 17 years, final ΔH working age +0.032; L1 ΔH −0.028 (ageing, strong mentoring) against −0.038 (stable, none).

---

3. Global sensitivity analysis (T03)

Same method as GADM-0.2.md § 5.1: Morris screening over all parameters, then Sobol indices (S1 Saltelli 2010 centred, ST Jansen, N = 256, bootstrap 95 % intervals) for the top 10 plus the reuse bundle; ±50 % ranges. New for GADM-0.4:

• the channel counters c_* are recalibrated for every sample (not varied), so each sample starts in equilibrium;
• the switch wealth_on is fixed;
• evaluation is parallel (--jobs 8). The parallel code reproduces the GADM-0.2 analysis exactly.

Full tables: simulations/gadm02/results/gsa_gadm04.md. Values are after the T04 equilibrium initialisation (§ 4). The analysis was rerun; values from before T04 are given where they differ materially.

	Baseline	Generative regime
Top ST	IX 0.26, dO 0.21, lX 0.10, q0 0.10, dX 0.08	expl 0.15, m 0.14, dO 0.14, q0 0.13, IX 0.12
ΣS1 (analysed set)	1.05	0.99 (before T04: 0.89)
Reuse bundle ΣS1 / ΣST	0.26 / 0.28	0.28 / 0.25
Morris σ ≈ or ≥ μ*	none	L (σ 0.048 vs. μ* 0.034), r_lin (0.045 vs. 0.043), m (0.057 vs. 0.062)
Clear Sobol interaction share (ST − S1)	none above 0.03	m +0.06

For comparison, GADM-0.2: baseline top ST was dO 0.34, p7 0.13, ell 0.10; generative top ST was m 0.27, L 0.13, ereg 0.13; ΣS1 ≈ 1.04–1.06.

3.1 Readings

1. The model is mostly additive, but the governance levers are strongly non-linear in the generative regime. For m, L and r_lin, the Morris spread σ is as large as their mean effect: their effect depends strongly on where in its range the lever sits, because the power channels have tipping points (GADM-0.3 P1/P2). Only membrane control shows a clear interaction share (ST − S1 ≈ 0.06). Overall, S1 sums to about 0.99. Correction: before T04, the generative regime summed to 0.89, and lineage diffusion looked purely interaction-driven (S1 ≈ 0). Part of that came from the non-equilibrium start. For these three levers, one-at-a-time analysis remains inadequate, because their effect depends on the operating point.
2. The commons cluster now drives the baseline. Commons investment IX, enclosure lX and obsolescence dX together carry about 0.43 of total ST. In GADM-0.2, the reuse bundle carried about 0.11. Two plausible reasons, not verified separately: durable capability makes X's contribution to formation persist, and the power channels make infrastructure control respond to X and standards. The bundle's effect is mostly IX alone, without interaction (ΣST ≈ ΣS1). So the GADM-0.2 correction ("reuse is not an interaction bundle") still stands.
3. The level of organism capability is a top uncertainty. dO stays among the top three drivers in both scenarios (ST 0.14–0.21), even after grounding. Varying dO with investment held fixed moves the organism level, which is exactly the parameter ParameterGrounding.md lists as ungrounded. Grounding that level is the most valuable next empirical step.
4. Explainability gains weight in the generative regime (ST 0.15, up from 0.06 in GADM-0.2). With power channels, epistemic capacity Q does double duty: it checks the political channel and feeds the discovery counter. So expl acts on power as well as on knowledge.

---

4. Equilibrium initialisation (T04)

4.1 Change

GADM-0.3 started wealth concentration at W = 0.5 and cohort attention at A_w = 0.45 and A_s = 0.65, none of which are equilibria. W then fell to about 0.25 and senior attention dropped by 0.25 in every run. That confounded comparisons against t = 0, and the wealth-tax effect (GADM-0.3.md §§ 3.4 and 5.3).

GADM-0.4 now:

• starts W, A_w and A_s at their equilibria with all other stocks fixed: W = 0.18, A_w = 0.54, A_s = 0.52 (aggregate A = 0.54). The values are computed by equilibrium_init(), hard-coded, and checked by a unit test;
• calibrates the channel counters at GADM-0.4's own initial state (gadm04.calibrate(), applied at import) instead of reusing GADM-0.3's. c_m and c_pol change, because they depend on W.

Every channel, W and the cohort attention stocks now start with |rate| < 10⁻⁴.

4.2 Effect on results

Result	before T04	after T04
Baseline / generative VFAC	0.325 / 0.665	0.320 / 0.660
Scenario ranking	—	unchanged
L1 (ageing, strong mentoring / stable, none)	−0.028 / −0.038	−0.029 / −0.039
L2 working-age A, care relief 0 → 0.15 (stable, care_sen 0.1)	0.399 → 0.441 (GADM-0.3)	0.403 → 0.446
L3 half-time, working-age capability	17 years	18 years
P3 ΔP, grounded / r = 0.45 / k_w = 0.20	−0.055 / −0.078 / —	−0.027 / −0.021 / −0.055
P3 holds in all robustness variants	5 of 6	6 of 6

4.3 Readings

1. The T02 P3 exception was an artifact. With W at its equilibrium (0.18 rather than 0.5), there is less wealth for a tax to remove, and no built-in decline of W leaking into the channels. P3 now holds in every variant, with smaller effects (ΔP −0.017 to −0.055).
2. The life-cycle readings are robust to initialisation. L2 was already evaluated as levels across settings, not against t = 0. It is confirmed with near-identical numbers.
3. Macro results move by at most 0.005 VFAC. The earlier confound affected comparisons against the starting point, not end states.

---

5. Agency per cohort (T05)

5.1 Structure

Effective agency G splits into working age G_w and seniors G_s. Both follow the GADM-0.2 form (existence security, access, restartability, organism access, minus domination and lock-in), with age-specific modifiers:

Modifier	Working age	Seniors
Restartability weight (risk capacity F)	1	0.5 (g3s)
Exposure to operator lock-in (K_Ω, 1 − m)	1	1.5 (b2s), digital exclusion
Care support from participation claims	—	gcare × relief
Inflow by ageing	young adults enter with G = 0.3 + 0.5 × children's durable capability	from working age

The aggregate G passed to the rest of the model is adult-weighted. Both stocks start at their equilibria (G_w 0.537, G_s 0.521; unit-tested). The switch cohort_G = 0 reproduces the T04 aggregate model (tested).

5.2 Results

Full tables: simulations/gadm02/results/agency.md. Aggregate VFAC changes by at most ±0.003 against the aggregate-agency model, so the macro readings are unaffected.

Scenario	G working age	G seniors	gap
Baseline	0.41	0.36	+0.048
Universal venture access	0.53	0.45	+0.081
Generative regime	0.70	0.69	+0.010
Generative, concentrated lineages	0.46	0.42	+0.038

5.3 Hypotheses

G1: operator-controlled membranes widen the senior agency gap. Supported, modestly. Raising user/community membrane control from 0.1 to 0.9 narrows the gap from +0.052 to +0.033, and raises both cohorts' agency (working age +0.09, seniors +0.11).

G2: education raises working-age agency with a generational lag. Not supported. Education +50 % raises working-age agency by only +0.005, with half the effect after 6 years. Agency is dominated by current conditions: security, domination, restartability. Entry agency depends on children's durable capability, which responds within about a decade. Education acts on reachable futures mainly through capability, not directly.

G3: care relief raises senior agency. Supported. Participation claims cp = 0.30 raise senior agency from 0.359 to 0.428. About 55 % of the gain comes through care relief, the rest through broader claims (which also raise working-age agency by +0.031).

5.4 Readings

1. Restartability-centred policy widens the age gap. Universal venture access raises working-age agency most, and leaves the largest senior gap (+0.081). Seniors benefit half as much from restartability.
2. The generative regime nearly closes the age gap (+0.010), mainly through user-controlled membranes (G1) and care relief (G3).
3. Post-work participation claims are an agency instrument for older people, not only an income instrument. More than half of their effect on senior agency runs through care relief.

5.5 Limitations

• The modifier values (g3s, b2s, gcare, entry agency) are assumed.
• Children's and the very old's agency are not modeled.
• The global sensitivity analysis in § 3 describes the model before T05. Given the ±0.003 VFAC change, the GSA was not rerun.

Reproduce

python3 -B -m unittest discover -s simulations/gadm02
cd simulations/gadm02 && python3 -B life_cycle.py --model gadm04
cd simulations/gadm02 && python3 -B robustness.py      # T02 robustness checks
cd simulations/gadm02 && python3 -B gsa.py --model gadm04 --jobs 8   # T03, ~12 min on 8 cores
cd simulations/gadm02 && python3 -B agency.py           # T05: cohort agency G1–G3