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

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 works as a bundle. The commons-rich regime is the strongest single-lever regime. However, rho0 alone is not a top driver in the sensitivity analysis (rank 20 of 80 at baseline). The effect comes from ρ together with investment I_X, open lineages and standards.
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 misses interactions. Finding 3 (reuse as a bundle) is an interaction effect. A global method (e.g. Sobol, or Morris screening) is a GADM-0.3 candidate.

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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.
• 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