assembly-economics/models/GADM-0.5.md

GADM-0.5 (in development)

Status: dev (2026-10-13), ASSEMBLY-WP-0007. Scope: models/GADM-Sectors-Scope.md. Module: simulations/gadm02/gadm05.py (gadm04 plus base-model parameters with defaults that reproduce GADM-0.4).

1. Rival and non-rival output (T01)

1.1 Structure

K_r = 1 − (1 − K_phys)(1 − φ_r K_Ω)        rival capacity (physical goods)
Y_r = K_r √E                                rival output, limited by ecological headroom
Y_n = (1 − φ_r) K_Ω (1 − K_phys)            non-rival output (information, organism services)
Y*  = Y_r + Y_n                             equals K at E = 1 for every φ_r

Non-rival output flows into commons X as far as it is openly inheritable: dX += xn · Y_n · lin_open · (1 − X). The ecological draw is unchanged (physical capital e1 K_phys dem thr plus compute energy e2 K_Ω). φ_r = 1 and xn = 0 reproduce GADM-0.4 exactly.

Priors: φ_r = 0.2 (organism use is dominated by information tasks, Anthropic Economic Index); xn = 0.3 (about the commons formation rate aX).

1.2 Results across the band (nonrival.py, results/nonrival.md)

Reading	B1	B2	B3	B4	class
ΔVFAC, GADM-0.5 vs. 0.4, baseline	+0.015	+0.026	+0.039	+0.049	quantitative shift
of which from the commons feed alone	+0.016	+0.026	+0.039	+0.049	quantitative shift
ΔE, baseline	−0.011	−0.020	−0.026	−0.025	quantitative shift
ΔVFAC, generative regime	+0.002	+0.003	+0.003	+0.007	quantitative shift
ΔE, generative regime	0.000	0.000	0.000	−0.002	—
generative − baseline VFAC (GADM-0.5)	+0.274	+0.308	+0.303	+0.266	robust

Non-rival share of output at t 100: baseline 0.12 (B1) to 0.66 (B4); generative regime 0.08 to 0.31.

1.3 Readings

1. The non-rival effect works through the commons, not through the output split. The whole VFAC gain comes from non-rival output feeding X. Calling output non-rival does not change VFAC, because VFAC measures stocks, not output.
2. The generative regime's ecological cost does not shrink (scope test 2: no). Its low ecological headroom (E 0.48 against 0.68–0.82 in the baseline) comes from the physical-capital and compute-energy draw, which a non-rival output share does not reduce. In the baseline, the commons feed even lowers E slightly, because more commons raise activity and throughput.
3. No genuine throughput decoupling (scope test 1: no). VFAC rises while throughput falls only in the baseline at B3–B4 (20–40 % of five-year windows), and there only because organism capability peaks and declines, taking its compute draw with it. The generative regime shows no decoupling.
4. What genuine decoupling would need: non-rival output that replaces rival consumption (dematerialization of demand), so that demand for physical throughput falls as Y_n grows. GADM has no demand composition. This is the question for the ecological classes (T02) and, if needed, sectors (T04). It is recorded as BR-010.
5. The generative regime's advantage is robust across the band (+0.27 to +0.31), slightly smaller than in GADM-0.4 (+0.29 to +0.34), because the baseline also gains from the commons feed.

2. Ecological resource classes (T02)

2.1 Structure

gadm05.simulate_eco replaces the single E with three classes, each with its own floor (0.20). The aggregate E used for output and VFAC is their geometric mean.

Class	Covers	Start (prior from indicators)	Regeneration
Er renewables	energy, land, freshwater, biosphere	0.15 (all three land/water/biosphere boundaries breached, PHC 2025)	yes, at rest at start
Ed materials	depletable stocks	0.45 (EU footprint 13.7 t/capita vs. a sustainable level of about 6–8 t)	none, or replenishment at rest (reserve growth)
Es sinks	climate, ocean acidification, novel entities, nutrient flows	0.33 (2 of 6 sink-type boundaries safe)	yes, at rest at start

Physical load (K_phys · dem · thr) draws 40/30/30 % on renewables, materials and sinks. Compute load (K_Ω) draws 30/20/50 %. The sink load is scaled by (1 − low-carbon electricity share, lowc 0.40, US-like). Recycling (circ 0.12, the EU circular material use rate) slows material depletion. These shares are priors.

2.2 Results (eco_classes.py, results/eco_classes.md)

	Strict material depletion	Material replenishment at rest
Renewables	below floor at start, every scenario	below floor at start, every scenario
Materials	cross the floor at t 25–70 (generative regime first, t 25–30)	never cross
Sinks	generative regime: t 29 (B4) to 83 (B1); others: never	same
Lowest class at t 100	materials	renewables

Organism growth (baseline, B4 − B1, relative change t 0 → 50): sinks −0.036, renewables −0.032, materials −0.018.

Low-carbon electricity share (generative regime, replenishment at rest): the sinks breach moves from t 49 / 37 / 29 (B2 / B3 / B4, share 0.4) to t 87 / 66 / 52 (share 0.7, EU-like). At 0.9 there is no breach within 100 years.

2.3 Readings

1. Grounding test passed: organism growth binds sinks and renewables (energy) before materials (ParameterGrounding.md § 3).
2. The renewables class is already beyond its floor at the start. The single E (0.235) hid that one class is binding today while the others still have headroom. Floors must be checked per class.
3. Which class binds next depends on an unmeasured assumption. With strict depletion, materials bind first in every scenario. If reserve growth keeps pace with use, they never do. Material replenishment needs grounding (reserve-to-production ratios over time).
4. The generative regime's ecological cost sits in the sinks. It is the only scenario that breaches the sink floor, and faster adoption brings the breach forward (t 83 → 29). This makes BR-010 more specific: the regime's problem is the sink load of its physical and compute activity, not output as such.
5. Low-carbon electricity is the decisive lever for organism-driven sink load. An EU-like share (0.7) delays the breach by 23–38 years; 0.9 avoids it within the horizon. The effect is conservative: sink regeneration is fitted so that each share starts at rest, so the lever acts only on the growth of the load.

3. Endogenous selection weights (T03)

gadm05.simulate_sel: the five selection weights (market, science, community, public, organism) relax at 0.05 per year toward targets pushed by domination and civic capacity:

• Domination: market concentration Pm raises the market weight, and discovery concentration Pd raises the organism weight (strength 2).
• Civic capacity: epistemic capacity Q raises science, and commons X raise community (strength 1).

Capture is the share of selection weight held by the market and organism channels. Results are in selection.py and results/selection.md.

Reading (endogenous − fixed weights, t 100)	B1	B2	B3	B4	class
capture, attention oligopoly	+0.114	+0.131	+0.138	+0.141	robust
capture, baseline	+0.023	+0.109	+0.115	+0.115	quantitative shift
capture, generative regime	−0.240	−0.211	−0.189	−0.161	robust
ΔVFAC, any scenario	≤ 0.002 in absolute value				no effect

Largest change in the weights themselves: 0.0044 per year.

Readings:

1. Domination captures selection, and the generative regime de-captures it. Both are robust across the band. Capture is slow and incremental, so the grounding test passes: no flip within a few years.
2. Capture of selection is not a material channel for VFAC in GADM. The five quality signals are of similar size, so moving weight between them hardly changes selection quality σ. A plural selection mix also does not make the generative regime more stable against a domination shock.
3. What would make it material: captured channels that deliver systematically worse quality. An example is organism selection under discovery concentration, O = Q (1 − disc), which falls only through the lever disc, not through the discovery channel Pd. Linking O to Pd is the candidate change. It is recorded here and not implemented, because no reading depends on it yet.

4. Two-good demand split: dematerialization (T04)

4.1 Structure

gadm05.simulate_eco(..., demand=...) adds a slow state Cr, the rival share of demand relative to its start (Cr0 = 1). Cr relaxes (rate dm_lam) toward 1 − dm_eps · (qn − qn0), where qn = (Y_n / Y*) · √(X Q) is the quality-weighted non-rival offer. Physical throughput intensity thr and physical investment aK scale with Cr. The same capital then serves dematerialized demand with less physical load. dm_lam = 0 switches it off. Priors: λ 0.05 per year, ε 2; strong case λ 0.1, ε 4.

4.2 Results (demand.py, results/demand.md; material replenishment at rest)

Generative regime	B1	B2	B3	B4
decoupled decades, no split → split → strong	0 → 0 → 0	0 → 0.45 → 0.89	0 → 0.71 → 0.75	0 → 0.22 → 0.34
sinks breach, no split → split → strong	t 83 → none → none	t 49 → 98 → none	t 37 → 61 → none	t 29 → 40 → none
VFAC t 100, no split → split	0.607 → 0.613	0.583 → 0.598	0.564 → 0.585	0.509 → 0.533

A decoupled decade has VFAC rising, physical throughput falling and K_Ω not falling, which excludes the artifact of § 1.3. Baseline: decoupled decades 0–0.09, and no sinks breach with or without the split.

Calibration check: throughput per unit of output falls by 0.1–0.5 % per year in the first decade (split cases included). Observed EU dematerialization is about 1.6 % per year (resource productivity, 2000–2022). The observed trend includes efficiency gains that GADM does not model.

4.3 Readings

1. Genuine decoupling needs demand composition. With the split, the generative regime decouples welfare from throughput in most decades at B2–B3, while organism capability keeps growing. BR-010 is resolved structurally: non-rival output must replace rival consumption, not only add to it.
2. Decoupling is band-dependent (qualitative break). It needs a non-rival offer of sufficient quality, which B1 adoption does not build. At B4, physical and compute growth outpace substitution, and decoupling is rarer (22 %).
3. The generative regime's sinks breach becomes avoidable. The default split moves it 15–50 years later. The strong split removes it within the horizon at every anchor. Together with low-carbon electricity (§ 2.3), this is the second lever on the regime's ecological cost.
4. The readings are conservative against data. Observed dematerialization (−1.6 % per year) is three to sixteen times the modeled intensity decline, because GADM has no autonomous efficiency trend. Calibrating ε, λ and an efficiency trend to the observed series is the next step (ASSEMBLY-WP-0018).