# 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. --- ## 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. --- ## 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 | --- ## 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. --- ## 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. --- ## 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. --- ## 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?