tegwick
08be42f65d
- MembraneModel, SeedKernel, EvolutionModel, MetabolismModel, ResourceOntology promoted verbatim from history/ with originating questions - NetworkObjectModel.md: implementation-neutral object model with invariants - Cross-lens notes applied from Glossary.md (T09) Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assistant: claude-code Assistant-Model: opus Assistant-Process: 1494246@bnt-lap001 Assistant-Session: caf6de6f-f9e4-47ce-a584-49baa271fa4b
431 lines
13 KiB
Markdown
431 lines
13 KiB
Markdown
<!--
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Created 2026-10-12 under ASSEMBLY-WP-0002-T08, as proposed in
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AdaptiveInformationProcessingNetworkConcept.md § 53.
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Sources: AdaptiveInformationProcessingNetworkConcept.md §§ 4–18, 21, 34,
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39–42; MembraneModel.md Part I §§ 8, 9, 11, 17, 18.
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-->
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# Network Object Model
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**Status:** v0.1, exploratory. Part of the information-organism lens (INTENT.md, Two Lenses).
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## 1. Purpose
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This document turns the entities of the AIPN concept into a precise object
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model: what objects exist, which fields they carry, how they relate, and which
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invariants hold between them.
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It stays **implementation-neutral**, as the AIPN concept § 52 requires. It does
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not choose a storage engine, graph database, programming language, message
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protocol or numeric representation. Field types are conceptual (`identifier`,
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`vector`, `time`, `reference`), not machine types.
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Normative keywords (MUST, SHOULD, MAY) carry over from the source concepts.
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---
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## 2. Notation
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```text
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ObjectName
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field: ConceptualType meaning
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field: [ConceptualType] zero or more
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field?: ConceptualType optional
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```
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`ref(X)` is a reference to an object of kind X by identity.
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`vector<D>` is a multi-dimensional value whose dimensions are named in D.
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---
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## 3. Common envelope
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Every object in the network carries the same envelope. It implements the
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provenance (§ 41), uncertainty (§ 42), governance envelope (Membrane § 9) and
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historical state (Membrane § 11) requirements once, instead of per object.
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```text
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Envelope
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id: identifier stable identity, independent of activation state
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kind: ObjectKind
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provenance: [ref(Evidence)] why this object exists or has its current state
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governance: GovernanceEnvelope
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valid_time: interval when the represented fact holds in the world
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record_time: interval when the network believed it (bi-temporal)
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confidence?: number absent = not assessed, which differs from 0
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learnable: LearnabilityMode see § 7
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```
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```text
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GovernanceEnvelope
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classification: label e.g. public, internal, confidential
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tenant?: identifier
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owner?: identifier
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purpose: [label] permitted purposes of processing
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retention?: policy-reference
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allowed_processing: [label]
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inherited_from: [ref(Envelope)] derived knowledge inherits restrictions
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```
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**Invariant E1.** A derived object's `governance` is at least as restrictive
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as the union of the envelopes it was derived from, unless an `Authority`
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explicitly relaxed it (§ 7).
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**Invariant E2.** Unknown is representable. A missing `confidence` or a value
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in the `unknown` state MUST NOT be read as zero or false.
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---
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## 4. Structure objects
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### 4.1 Node
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```text
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Node : Envelope
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semantic_kind: NodeKind EntityType | State | Role | Capability | Process |
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Resource | Context | Goal | Risk | Observer |
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Policy | Concept | Chunk
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label: text
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state: NodeState
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```
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```text
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NodeState
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activation: ActivationState
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statistics: vector<frequency, recency, ...>
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predictors: [ref(PredictionModel)]
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utility_estimates: vector<goal dimensions>
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```
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### 4.2 Edge
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```text
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Edge : Envelope
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source: ref(Node)
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target: ref(Node)
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relation: RelationType is-a | part-of | uses | requires | precedes |
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associated-with | predicts | enables | inhibits |
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routes-to | observed-by | contributes-to | ...
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context?: [ref(Context)] A + Context X → B is a different edge from A → B
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properties: EdgeProperties
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activation: ActivationState
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```
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```text
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EdgeProperties (AIPN § 21: four independent properties)
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association: number co-occurrence strength
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prediction: number transition / conditional probability
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utility: vector<goal dimensions> e.g. correctness, latency, cost
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confidence: number evidence-weighted trust in the above
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```
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**Invariant S1.** `association`, `prediction`, `utility` and `confidence` MUST
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be stored and updated independently. No operation may collapse them into one
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scalar.
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### 4.3 Chunk
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```text
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Chunk : Node semantic_kind = Chunk
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members: [ref(Node | Edge | Chunk)]
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pattern: PatternDescription sequence, subgraph, recurring context, workflow, ...
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formation: ref(Episode) episodes that justified formation
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```
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A Chunk is a Node and can be activated, linked and chunked again (hierarchy).
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Across lenses, a chunk is an *assembly at organism scale* (Glossary).
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### 4.4 Context
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```text
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Context : Node semantic_kind = Context
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condition: ConditionDescription
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```
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---
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## 5. Activity objects
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### 5.1 Token
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```text
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Token : Envelope
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source_binding?: ref(SourceBinding) absent for internally generated tokens
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external_ref?: identifier identity in the system of record
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persistence: transient | persistent | external | reconstructed
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activates: [ref(Node)] one token may activate many nodes
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```
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### 5.2 ActivationState
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Activation is *state*, not an object with its own identity.
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```text
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ActivationState
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level: number
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sources: [ActivationSource] token | neighbor | goal | context | prediction |
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surprise | attention | regulation | memory
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decay: DecayRule activation decays unless sustained
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as_of: time
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```
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### 5.3 Episode
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```text
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Episode : Envelope
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inputs: [ref(Token)]
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context: [ref(Context)]
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activations: [ActivationRecord] node or edge, level, time
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predictions: [ref(Prediction)]
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actions: [ref(Action)]
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observations: [ref(Claim)]
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signals: [ref(Signal)]
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boundary: interval
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```
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Episodes are the unit of learning, replay, explanation, attribution and audit.
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### 5.4 Prediction
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```text
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Prediction : Envelope
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made_in: ref(Episode)
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subject: ref(Node | Token | Context)
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expectation: ExpectationDescription occurrence OR absence
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horizon: interval
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resolution?: ref(Claim) the observation that confirmed or refuted it
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error?: vector<dimensions>
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```
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### 5.5 Action
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```text
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Action : Envelope
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kind: ref(Node) e.g. RouteToHumanReview, Escalate
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target?: ref(Token | SourceBinding)
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state: ActionState
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state_history: [(ActionState, time, ref(Authority)?)]
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caused_by: ref(Episode)
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```
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```text
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ActionState = possible | permitted | recommended | selected | executed | mandatory
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```
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**Invariant A1.** These states MUST NOT be treated as equivalent. A
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transition into `permitted` or `mandatory` MUST name an `Authority`; the
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network may not grant these states to itself.
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**Invariant A2.** "Something happened" (a Claim) and "the network caused
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something to happen" (an executed Action) are distinct objects.
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### 5.6 EligibilityTrace
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```text
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EligibilityTrace
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subject: ref(Node | Edge | Prediction | Action)
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episode: ref(Episode)
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strength: number
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decay: DecayRule
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```
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Like ActivationState, a trace is temporary state used for delayed credit
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assignment, not a durable object.
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---
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## 6. Evaluation objects
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### 6.1 Observer
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```text
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Observer : Node semantic_kind = Observer
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observes: [ref(Node | Goal | ViabilityVariable)]
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method: EvaluationDescription
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operator?: human | artificial | institutional
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```
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Multiple observers MAY evaluate the same episode differently. Their signals
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are kept side by side, not averaged at the source.
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### 6.2 Signal
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```text
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Signal : Envelope
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emitted_by: ref(Observer)
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about: ref(Episode)
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kind: success | failure | reward | penalty | prediction-error | novelty |
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surprise | constraint-violation | human-feedback | viability-deviation
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value: vector<dimensions> multi-dimensional by default
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```
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**Invariant V1.** A signal's `value` MUST stay multi-dimensional through
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storage. Any aggregation is a separate, explicit step that records its
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weights.
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### 6.3 Goal
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```text
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Goal : Node semantic_kind = Goal
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tendency: GoalDescription desired condition, outcome or constraint
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source: ref(Authority) goals are received from the environment
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priority?: number
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conflicts: [ref(Goal)] conflicts stay visible, never silently flattened
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```
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### 6.4 ViabilityVariable
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```text
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ViabilityVariable : Node
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measure: ref(Node | SourceBinding)
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corridor: range acceptable operating range
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floor_kind: corridor | constitutional
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current: value
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```
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`constitutional` corridors are non-tradeable floors. They correspond to GADM's
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constitutional constraints (Glossary, "Viability").
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**Invariant V2.** Viability precedes optimization. No utility gain may justify
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an action that is predicted to push a `constitutional` variable out of its
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corridor.
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---
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## 7. Governance objects
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### 7.1 Authority
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```text
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Authority : Envelope
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holder: human | institution | delegated-role
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scope: [label] what it may permit, mandate, relax or make learnable
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granted_by?: ref(Authority)
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```
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### 7.2 Learnability
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```text
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LearnabilityMode = learnable | learnable-with-authority | frozen
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```
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**Invariant G1.** The learning operation `L` (§ 9) MUST NOT modify an object
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whose mode is `frozen`. It may modify a `learnable-with-authority` object only
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when a referenced Authority covers that scope (AIPN § 39: learning and
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authority are distinct).
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---
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## 8. Membrane objects
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### 8.1 SourceBinding
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```text
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SourceBinding : Envelope
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semantic_entity: ref(Node)
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source_system: identifier
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source_type: label
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identity_mapping: MappingDescription
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projection_rule: ProjectionDescription read model, not a replica
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access_policy: PolicyReference
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freshness_policy: PolicyReference
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write_capabilities: [label] what actions may write back, if anything
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membrane_layer: constitutional | operational | adaptive | ephemeral
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```
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### 8.2 Claim
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```text
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Claim : Envelope
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subject: ref(Token | Node)
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predicate: label
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object: value | ref(Token | Node)
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source: ref(SourceBinding | Observer)
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authority?: ref(Authority)
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observed_time: time
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```
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Claims allow contradictions: two claims with the same subject and predicate
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and different objects can coexist without corrupting the model.
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### 8.3 Evidence
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```text
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Evidence : Envelope
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supports: [ref(Claim | Node | Edge | Chunk)]
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origin: ref(SourceBinding | Episode | Observer)
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locator: identifier pointer into the source; the artefact stays at its source
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```
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**Invariant M1.** Every semantic assertion entering through the membrane is a
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Claim backed by Evidence. Learning MUST NOT remove provenance.
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---
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## 9. Operations
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The formal model of AIPN § 34 is \(N=(V,E,T,G,O,H)\). The objects map onto it
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as follows:
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| Formal | Objects |
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|---|---|
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| \(V\) nodes | Node, Chunk, Context, Goal, Observer, ViabilityVariable |
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| \(E\) edges | Edge (with EdgeProperties) |
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| \(T\) tokens | Token |
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| \(G\) goals | Goal, ViabilityVariable |
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| \(O\) observers | Observer |
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| \(H\) history | Episode, Prediction, Action, Signal, Claim, Evidence (bi-temporal) |
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The two operations:
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```text
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evaluate: O(τ, G) → [Signal] observers evaluate an episode against goals
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learn: L(N, τ, [Signal]) → N' the network changes, subject to G1, M1, S1, V1
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```
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`learn` covers the four core learning modes (associative, predictive,
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instrumental, structural) and optionally regulatory learning. Structural
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learning creates or removes Edges and Chunks. Every structural change produces
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Evidence that references the Episodes justifying it.
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---
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## 10. Worked example: invoice processing
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```text
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Token inv-4711 (SourceBinding: SAP AccountingDocument)
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activates Invoice, SupplierInvoice, HighValueTransaction, Supplier:Acme, MonthEnd
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Edge Invoice --routes-to--> HumanReview context: [MonthEnd]
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properties association 0.42, prediction 0.18, utility {correctness .94, latency .31, cost .22}, confidence .91
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Prediction "approval within 2 days" horizon 2d
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Action RouteToHumanReview state: recommended → selected → executed
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permitted by Authority: AP-Policy-2026
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Claim inv-4711 approved-at 2026-10-03 source: SAP binding
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Signal Observer:LatencyObserver → {latency: -0.4, correctness: +0.9}
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learn edge confidence and utility updated; Evidence → episode ep-1182
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```
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---
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## 11. Extension points
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The following belong to other organism-lens concepts and are left as named
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extension points, so this model can grow without reshaping its core:
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| Extension | From | Expected objects |
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|---|---|---|
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| Membrane layers and sampling | MembraneModel.md | Membrane, SamplingPolicy, Habituation |
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| Development | SeedKernel.md | Kernel, DevelopmentalStage |
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| Evolution | EvolutionModel.md | Genotype, Phenotype, Lineage, FitnessAssessment |
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| Metabolism | MetabolismModel.md | MetabolicBudget, SubstrateFlow, ResourceAccount |
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| Resource ontology | ResourceOntology.md | ResourceClass, ConversionFunction, SentientBoundary |
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## 12. Open questions
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1. Should Activation be modeled as an event stream (replayable) or only as
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current state plus Episode records?
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2. Is `Context` best treated as a Node kind or as an edge qualifier only?
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3. What is the minimum bi-temporal guarantee: full valid-time history, or
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only record-time for audit?
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4. How should a Signal's dimensions be governed, so that observers stay
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comparable without forcing a fixed schema?
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