assembly-economics/models/GADM-0.2.md

Generative Assembly Dynamics Model — GADM-0.2

Status: exploratory hypothesis generator. Not calibrated to any real economy. All numbers are in normalized, unitless model space.

GADM-0.2 extends GADM-0.1 (GenerativeAssemblyDynamicsModel.md) in three ways:

1. It specifies every flow with signs (see GADM-CLD.md).
2. It adds the information-organism extensions selected below.
3. It is implemented as a runnable toy simulation.

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1. Decision: which organism extensions enter GADM-0.2 (T03)

Candidates from InformationOrganismsInGADM.md § 4:

Candidate	Decision	Rationale
Split K into K_phys + K_Ω	in	Learning with use (R7) and lineage control (R8) are the main new loops. Without the split, they cannot be expressed.
Attention headroom A	in	Reference scarce resource of the program. Needed to test whether VFAC without A misses attention harm.
Organism selection channel O	in	Cheap to add. Needed to examine hypothesis M3 (O's quality and its contribution to P share a cause).
Membrane control and lineage concentration in Ṗ	in, as levers m and L	Main institutional levers of the organism lens.
Participation claims C_p	in, as lever cp	Lets scenarios test a post-work claim channel. It is treated as a share of capital return, which is a simplification (see § 6).
Compute C as its own stock	deferred to 0.3	Represented for now as a direct draw of K_Ω on E (e2). A separate stock only pays off once energy classes are modeled (GADM-0.4).
Age cohorts	deferred to 0.3	See GADM-0.3-Candidates.md.

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2. State

Twelve stocks, all in [0, 1]:

Symbol	Code	Stock	New in 0.2
H	H	Human capability
X	X	Assembly commons
K_phys	Kp	Physical / conventional productive capacity	split
K_Ω	Ko	Information-organism capability	✓
F	F	Risk capacity
G	G	Effective agency
N	N	Diversity & composability
Q	Q	Epistemic capacity
P	P	Domination concentration
E	E	Ecological headroom
S	S	Social cohesion	now a stock relaxing to a target
A	A	Attention headroom: unclaimed, self-directed attention	✓

Bounding convention. Every stock follows \dot x = \text{inflow}\cdot(1-x) - \text{outflow}\cdot x, so stocks stay in [0, 1] without clipping. Clipping is applied only as a numerical guard.

Institutional levers (scenario axes, all in [0, 1]):

Lever	Meaning
m	membrane control held by users and communities rather than operators
L	lineage diffusion: 0 = concentrated, 1 = diffuse ownership of organism lineages
lin_open	share of lineages that are openly inheritable (commons rather than enclosure)
std	open standards and interoperability
disc	discovery concentration
expl	explainability and provenance enforcement
x_att	attention regime: 0 = protected, 1 = extraction-funded
auto, dshare, cp, od	automation intensity, social dividend share, participation claims share, ownership diffusion of private returns

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3. Equations

3.1 Auxiliaries

[ K = 1-(1-K_{phys})(1-K_\Omega), \qquad Y^* = K\sqrt{E} ]

Claims (GADM-0.1 § 9, extended with C_p):

[ w_L = w_{L0}(1-auto\cdot K_\Omega),\quad c = 1-w_L,\quad \Pi_p = c,(1-dshare-cp) ] [ B = w_L + c,(dshare+cp) + \Pi_p\cdot od ]

B is the breadth of claims: the share of output reaching the broad population as wages, dividends, participation claims or diffused ownership. Demand factor: dem = (0.4+0.6B)(0.6+0.4S).

Experimentation (GADM-0.1 § 5, plus a Q-gated organism term, hypothesis M2):

[ V = \min!\left(1,; v_0 H^{.5}X^{.5}F^{.3}G^{.4}N^{.3}Q^{.3}(1-P)^{.5} + v_o K_\Omega Q\right) ]

Selection (GADM-0.1 § 6, plus organism channel O weighted by prevalence):

[ M=\sqrt{A\cdot dem},\quad O = Q(1-disc) ] [ \sigma = \frac{w_m M + w_s Q + w_c N(1-P) + w_p S + w_o K_\Omega O}{w_m+w_s+w_c+w_p+w_o K_\Omega}, \qquad V_{sel} = V\sigma ]

Reuse: \rho = \min(1, \rho_0 + \rho_O K_\Omega\, lin\_open).

3.2 Stocks

[ \begin{aligned} \dot X &= (a_X\rho V_{sel} + I_X)(1-X) - (\delta_X + l_X P + l_{Xo}K_\Omega(1-lin_open))X\ \dot K_{phys} &= a_K V_{sel},dem,(1-K_{phys}) - \delta_K K_{phys}\ \dot K_\Omega &= (a_O V_{sel},dem + \ell K_\Omega,dem,Q + o_{seed})(1-K_\Omega) - \delta_O K_\Omega\ \dot H &= (h_0(\tfrac{S+B}{2}) + h_A A + h_T X)(1-H) - (\delta_H + desk\cdot K_\Omega(1-m))H\ \dot F &= (f_0 + f_Y Y^* B)(1-F) - \delta_F F\ \dot G &= (g_1B + g_2X(1-P) + g_3F + g_4K_\Omega m)(1-G) - (\delta_G + b_1P + b_2K_\Omega(1-m))G\ \dot N &= (n_1V(1-P) + n_2X,std)(1-N) - (\delta_N + n_3P + n_4K_\Omega(1-L))N\ \dot Q &= (q_0 + q_1X + q_oK_\Omega,expl)(1-Q) - (\delta_Q + q_2P + q_xK_\Omega(1-expl) + q_3\max(0,V-Q))Q\ \dot P &= (p_1V_{sel}(1-B) + p_2,disc + p_3K_\Omega(1-m) + p_4K_\Omega(1-L))(1-P) - (p_5QS + p_6X,std + p_7B)P\ \dot E &= e_{reg}(1-E) - (e_1K_{phys},dem,thr + e_2K_\Omega)E\ \dot S &= s_{rate}\left(G^{.4}(1-P)^{.3}B^{.2}A^{.1} - S\right)\ \dot A &= a_{rec}(0.4+0.6B)(1-A) - a_{ext},x_att,(0.2+K_\Omega)A \end{aligned} ]

Parameter values are in simulations/gadm02/gadm02.py (PARAMS, INIT).

3.3 Diagnostics

[ VFAC = \left(H,X,G,N,Q,S,E,(1-P),A\right)^{1/9},\qquad VFAC_{0.1} = \left(H,X,G,N,Q,S,E,(1-P)\right)^{1/8} ]

These use equal exponents. VFAC is 0 if any factor is 0. As in GADM-0.1, VFAC is a research instrument, not a welfare function.

Constitutional floors, checked separately: G \ge 0.2,\; E \ge 0.2,\; P \le 0.7,\; A \ge 0.2.

3.4 Calibration

Time unit: one period = one year (ASSEMBLY-WP-0005-T01), so all rates are per year and the horizon is 100 years. The implied timescales are mostly plausible: commons obsolescence about 25 years (dX), physical capital about 20 years (dK), cohesion adjustment about 5 years (srate). Organism capability, at about 17 years (dO), is the main exception (§ 5.1).

There is no empirical calibration. Three coefficients were adjusted so that the baseline stays near its initial state, instead of trivially collapsing or saturating:

• v0 = 2.5: the seven-factor product is about 0.14 at initial values.
• aK = 0.35 and n1 = 0.30: otherwise K_phys and N decay to about 0.12.

Everything else is a first guess, chosen so that rates fall roughly in 0.03–0.3 per period.

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4. Scenario results (T05)

Full table: simulations/gadm02/results/summary.md. Scenario definitions: scenarios/. Final values at t = 100:

Scenario	VFAC	VFAC_0.1	P	G	A	B	Notes
Baseline	0.46	0.44	0.52	0.44	0.65	0.59	slow erosion of X and N, rising P
Automated capitalism	0.32	0.29	0.73	0.28	0.58	0.33	P ceiling breached in 69 % of periods; demand and cohesion fall
Dividend abundance	0.45	0.43	0.54	0.45	0.62	0.69	demand stabilized, but VFAC ≈ baseline
Universal venture access	0.49	0.47	0.52	0.57	0.64	0.58	agency up via restartability
Commons-rich	0.54	0.54	0.48	0.47	0.62	0.57	X 0.27 → 0.66; second best overall
Attention oligopoly	0.37	0.36	0.69	0.38	0.55	0.61	P approaches the ceiling; N collapses to 0.17
Generative regime	0.65	0.64	0.31	0.64	0.78	0.70	stable; lowest ecological headroom (E 0.48)
— with concentrated lineages	0.53	0.50	0.50	0.46	0.79	0.71	loses about two thirds of the gain over baseline
— with attention extraction	0.58	0.59	0.41	0.60	0.45	0.71	VFAC_0.1 understates the loss

Readings

These describe the model's behavior under its assumed signs. They are candidate hypotheses, not claims about economies.

1. Abundance without breadth fails (automated capitalism). Output Y* is close to the baseline (0.50 vs. 0.53), but domination crosses its ceiling and claims breadth B drops from 0.59 to 0.33. This reproduces GADM-0.1's primary question in model form.
2. Dividends stabilize demand, not agency. Dividend abundance raises B from 0.59 to 0.69, but G, P and VFAC barely move. In this model, claims alone do not counter domination or commons erosion.
3. Reuse matters through large shifts, not interaction. The commons-rich regime is the strongest single-lever regime, but rho0 alone is not a top driver (rank 20 of 80 one-at-a-time). The original reading, "reuse works as a bundle", was refuted by the global analysis (§ 5.1): the bundle shows almost no interaction. The scenario's effect comes from moving I_X, ρ_0 and lin_open by 100–200 %, far outside local sensitivity ranges.
4. Organism governance dominates the generative regime. Concentrating lineages (L, lin_open, m low) costs about two thirds of the generative regime's VFAC gain over baseline (0.65 → 0.53, baseline 0.46). An extraction-funded attention regime costs about 40 % (0.65 → 0.575).
5. VFAC without attention understates attention harm. Moving from the generative regime to its attention-extraction variant, VFAC falls by 0.075, but VFAC_0.1 falls by only 0.042 (0.636 → 0.594). In the extraction variant, VFAC_0.1 even ranks above VFAC, the reverse of every other scenario. This supports including A, and also the Goodhart warning in InformationOrganismsInGADM.md § 4.1.
6. The generative regime has an ecological cost. It has the lowest E of all regimes. It stays above the floor, but ereg ranks third in its sensitivity. Abundance needs throughput decoupling (GADM-0.1 § 14) to stay generative.

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5. Sensitivity (T06)

Full tables: simulations/gadm02/results/sensitivity.md (one-at-a-time, ±20 %, effect on final VFAC).

Rank	Baseline	Generative regime
1	dO organism depreciation (+)	m membrane control (+)
2	ell organism learning (−)	L lineage diffusion (+)
3	p7 claims breadth checking P (+)	ereg ecological regeneration (+)
4	wL0 labor share (+)	expl explainability (+)
5	IX commons investment (+)	v0 experimentation (+20 % → −)

Readings:

• Under baseline governance, faster organism learning lowers VFAC. Higher ell and lower dO both reduce VFAC: K_Ω grows under operator-controlled membranes (m = 0.3) and concentrated lineages, and this feeds P, lock-in and deskilling. In the generative regime, the same organism growth is beneficial, and the governance levers m and L become the top drivers. This is the model form of hypothesis M3. Caveat: it follows directly from the assumed moderation of links #39–#41 by m and L. The model shows that these assumptions matter most, not that they are true.
• More experimentation can hurt in the generative regime (+20 % v0 → lower VFAC). This happens through the epistemic brake B3 (link #33, a contested link).
• One-at-a-time analysis cannot detect interactions. The global analysis in § 5.1 checks for them.

5.1 Global sensitivity (ASSEMBLY-WP-0005-T01)

Full tables: simulations/gadm02/results/gsa.md. Method: Morris screening over all 80 parameters (20 trajectories), then Sobol indices for the top 10 plus the reuse bundle. Parameters vary ±50 %, N = 256, with bootstrap 95 % intervals. S1 uses the Saltelli (2010) estimator with centred outputs; ST uses the Jansen estimator. The uncentred S1 estimators were tried first and were unusable (VFAC's mean is large relative to its spread).

	Baseline	Generative regime
Top ST	dO 0.34, p7 0.13, ell 0.10, wL0 0.07, IX 0.07	m 0.27, L 0.13, ereg 0.13, p7 0.07, expl 0.06
ΣS1 (analysed set)	1.04	1.06
Reuse bundle ΣS1 / ΣST	0.09 / 0.11	0.08 / 0.09

Readings:

1. The model is nearly additive within ±50 %. ST ≈ S1 for every analysed parameter, and ΣS1 ≈ 1. Interactions are small, so the one-at-a-time rankings in § 5 are confirmed.
2. The "reuse as a bundle" reading is refuted. The bundle explains about 10 % of variance, with negligible interaction (ΣST − ΣS1 ≈ 0.01–0.02). Reading 3 in § 4 has been corrected.
3. Organism governance remains the dominant lever in the generative regime. Membrane control m alone explains roughly 27–32 % of variance, and lineage diffusion L a further 13–16 %. Reading 4 in § 4 holds globally.
4. Calibration priority: dO. Organism-capability depreciation explains about a third of baseline variance. At 0.06 per year it implies a 17-year lifetime, which is plausibly too long for AI systems. It should be calibrated first.

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6. Known limitations

• No units, no calibration. Comparisons between scenarios are meaningful; absolute values are not.
• Every structural sign comes from GADM-CLD.md. The contested links (#33, #40, #41, #46, #49) drive several readings.
• Participation claims cp are a fixed share of capital return. A real mechanism would tie them to contributions (attention, judgment, governance), which is WS-14 work.
• A single population with no cohorts, sectors or regions.
• Attention dynamics are weak: in the attention oligopoly, A falls only to 0.55. aext may be underestimated. Resolved in GADM-0.3 (GADM-0.3.md § 4.1: aext recalibrated to 0.275 against time-use evidence).
• Euler integration, dt = 0.1. No stiffness issues were observed at these rates.

7. Reproduce

python3 -m unittest discover -s simulations/gadm02
cd simulations/gadm02 && python3 run_scenarios.py && python3 sensitivity.py
cd simulations/gadm02 && python3 gsa.py          # ~5 min; --quick for a smoke run