assembly-economics/models/InformationOrganismsInGADM.md

Information Organisms in GADM — Mapping Note v0.1

Purpose

GADM-0.1 models a society's assembly dynamics with ten stocks. It treats automation only as part of Productive Capacity K. The information-organism lens describes machine capacity as populations of adaptive, governed, evolvable systems: AIPNs with membranes, metabolism and lineages.

This note asks two questions:

1. Where do information organisms enter GADM-0.1's existing stocks and flows?
2. Which variables does GADM-0.1 lack once organisms are taken seriously?

Everything below is a hypothesis for GADM-0.2 and later, not a result.

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1. Why organisms are not just more K

GADM-0.1 has \dot K = a_K V_{commercial} - \delta_K K. Conventional capital depreciates and is shaped from outside. Information organisms differ in four ways that matter dynamically:

Property	Conventional K	Information organisms
Change with use	depreciates	learns: may appreciate with use
Reproduction	built anew	inherits and recombines learned structure across generations
Boundary	none	membrane: what it senses and touches is governed and configurable
Inputs	capital, energy, labor	metabolism: compute, information, authority, and human attention

Hypothesis M1. Folding organisms into K hides the two loops that matter most for generativity: learning-with-use (a reinforcing loop inside K) and lineage ownership (a reinforcing loop into P).

Proposal. Split productive capacity in GADM-0.2:

[ K = K_{phys} + K_{\Omega} ]

where K_{\Omega} is the capability of the information-organism population \Omega, with

[ \dot K_{\Omega} = a_{\Omega} V_{commercial} + \ell \cdot U_{\Omega} \cdot Q - \delta_{\Omega} K_{\Omega} ]

where:

• U_{\Omega} = utilization (organisms learn only when they run),
• \ell = learning yield per unit of utilization,
• Q multiplies learning: organisms learn from signals only as well as the observers and evidence around them allow.

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2. Stock-by-stock mapping

Stock	How organisms raise it	How organisms lower it	Lever from the organism lens
H Human Capability	tutoring, cognitive augmentation, freeing time for development	deskilling, loss of tacit knowledge when humans stop practicing	route-to-human actions, symbiosis rather than autonomy
X Assembly Commons	open lineages and inheritable learned structure become reusable components (I_X, raises \rho)	proprietary lineages and closed membranes act as enclosure (L_X)	breeding regime: who may inherit from which lineage
K Productive Capacity	see § 1	metabolic waste, over-provisioned compute	return-on-compute regulation
F Risk Capacity	automated underwriting, cheap simulation of experiments before real risk	correlated failures when many actors use the same lineage	diversity of lineages
G Effective Agency	personal or community-owned organisms raise Access and Restartability	LockIn through membranes controlled by others; delegation without exit	membrane portability, the right to own one's organism
N Diversity & Composability	recombination along interfaces; generalists and specialists coexisting	monoculture from one dominant lineage	interface standards for recombination
Q Epistemic Capacity	Research, Audit, OpenEvidence via observers, provenance and explainability	Opacity, Manipulation, InformationCapture	explainability and provenance as hard requirements
P Domination Concentration	—	DiscoveryControl, InfrastructureControl; ownership of lineages compounds	membrane interoperability, lineage ownership diffusion
E Ecological Headroom	efficiency gains, better ecological sensing	compute as energy draws directly on physical throughput	metabolic budgets tied to f(E)
S Social Cohesion	abundance that is broadly shared, fewer attention conflicts	attention extraction, loss of meaningful participation, opaque authority	attention protection; authority stays with humans

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3. Flow-level entry points

3.1 Experimentation V

Organisms lower the cost of serious experiments. This raises the baseline v_0, and partly decouples V from H and F.

Hypothesis M2. Because GADM-0.1 notes that V\uparrow with Q\downarrow accelerates damage, organism-driven experimentation is generative only where Q rises with it. In GADM-0.2, the organism contribution to V should therefore be gated by Q, not just added.

3.2 Selection \sigma

Organisms add a fifth selection channel:

[ \sigma = w_m M + w_s Sci + w_c Com + w_p Pub + w_o O ]

where O is organism-mediated selection: ranking, recommendation, screening, automated review. Observers and multi-dimensional fitness from the evolution layer make O potentially high quality. However, O is also exactly the DiscoveryControl term in \dot P.

Hypothesis M3. The quality of O and its contribution to P are determined by the same variable: who configures the observers and membranes.

3.3 Preservation and reuse \rho

Inheritance of learned structure, including failures, is a preservation mechanism with its own conversion efficiency into X. Whether it counts as I_X (commons) or feeds L_X (enclosure) depends on lineage licensing.

3.4 Claims on output I

GADM-0.1 decomposes claims as I = W + \Pi_p + D_s. Organisms push W/Y down, and route their returns to whoever owns the lineage. That concentrates \Pi_p unless ownership is diffuse.

The post-work participation question in INTENT.md suggests a fourth term:

[ I = W + \Pi_p + D_s + C_p ]

where C_p = participation claims: claims arising from supplying what organisms cannot supply themselves, such as objectives, judgment, consent, evaluation as observers, and governance. Whether C_p is a real distribution channel or a relabeled dividend is an open research question (WS-14).

3.5 Domination \dot P

Two terms to add explicitly:

[ \dot P = \dots + MembraneControl + LineageConcentration

• MembranePortability - LineageDiffusion ]

The success → resources → control loop in GADM-0.1 § 13 is sharper for organisms: a dominant lineage learns from the most use, so it improves fastest and attracts the most use.

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4. Variables GADM-0.1 lacks

#	Candidate	Type	Why it is needed
1	Attention headroom (A)	stock	Human attention is the reference scarce resource. It drives adoption M, observer quality in O, participation claims C_p and cohesion S. Organisms draw on it as premium energy. GADM-0.3 already plans to "model adoption and attention separately".
2	Organism capability (K_{\Omega})	stock	Learning-with-use and lineage dynamics (§ 1).
3	Compute (C)	flow or stock	The metabolic link between K_{\Omega} and E. It makes the energy cost of machine cognition explicit instead of hidden in f(E).
4	Membrane governance distribution	parameter of P and G	Who configures what organisms sense and do. The main control interface in the organism lens.
5	Participation claims (C_p)	flow	The post-work participation mechanism (§ 3.4).
6	Authority boundary	constraint, not a stock	Learning is distinct from authority. Like VFAC's constitutional constraints, it bounds optimization and must not be traded off inside it.

4.1 Consequence for VFAC

If A becomes a stock, it is a candidate factor in VFAC:

[ VFAC = H^a X^b G^c N^d Q^e S^f E^g (1-P)^h A^i ]

A must mean unclaimed, self-directed attention, not attention captured by products. Otherwise maximizing VFAC would reward attention extraction, the opposite of the intent. This is a concrete Goodhart risk to record in the metrics catalog.

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5. Proposed scenario dimension

INTENT.md calls for an information-organism dimension in a later scenario set. Using the variables above, three axes are enough to start:

1. Lineage ownership: concentrated ↔ diffuse
2. Membrane control: operator-controlled ↔ user/community-controlled and portable
3. Attention regime: extraction-funded ↔ protected headroom with participation claims

Crossing these with the six GADM-0.1 scenarios (for example, Attention oligopoly is concentrated / operator-controlled / extraction-funded) gives a first organism-aware scenario grid.

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6. Open questions

1. Does learning-with-use make K_{\Omega} a natural monopoly in the absence of lineage diffusion, and what interoperability level breaks that loop?
2. Is C_p measurable separately from D_s?
3. Can organism-mediated selection O raise Q without also raising P?
4. What is the right unit of A: time, quality-weighted time, or reachable self-directed activities?
5. Where does the organism lens's "viability precedes optimization" map formally: onto VFAC's constitutional constraints, or onto the floors of individual stocks?