Exploration: Info-Comp Core Revision — Tracing the History and the Evaluator Question
Status: Exploration. Reviewing the original info-comp core, comparing to information theory, and determining whether the evaluator distinction should have been caught.
1. The two primitive sets and their origins
1.1 Info-comp core (from v1 analysis)
Primitives: {St (State), Str (Structure), Tr (Transformation), Me (Measure), Co (Constraint), Pa (Parameter)}
Derived from: Integrating IT, CC, DS, NT — finding what information theory, computational complexity, dynamical systems, and network theory all share.
How it was derived: Each primitive abstracts across four domains:
- St: Σ (IT symbols), Π (CC problems), Ω (DS states), V (NT nodes)
- Str: P (IT distributions), M-model (CC machines), Ω-structure (DS metrics), T (NT topology)
- Tr: C+K (IT channel+code), A (CC algorithm), φ (DS flow), E+F (NT edges+flow)
- Me: μ (IT entropy), R (CC resource), I (DS invariant), centrality (NT)
- Co: Γ (IT capacity), R-bounds (CC complexity), conservation (DS), connectivity (NT)
- Pa: noise (IT), input size (CC), λ (DS control), W (NT weights)
Core triad: {St, Tr, Me} — "what is a stateful measurable dynamic system?"
1.2 Information theory (from v1 analysis, revised in v2)
v1 primitives: {Σ, P, C, K, μ, Γ} — Symbol, Distribution, Channel, Code, Measure, Constraint
v2 revised primitives: {Σ, P, C, Ke, Kd, μ, Γ} — K split into Encoder (Ke) + Decoder (Kd)
Derived from: Shannon's mathematical theory of communication and its extensions.
1.3 They are NOT the same domain
The info-comp core is the ABSTRACTION of four domains (IT, CC, DS, NT). Information theory IS one of those four domains. The relationship is:
Info-comp core {St, Str, Tr, Me, Co, Pa}
↓ configuration
Information Theory {Σ, P, C, Ke, Kd, μ, Γ} (= IT at specific settings)
Computational Complexity {Π, I, A, M, R, Cl}
Dynamical Systems {Ω, x, φ, t, λ, I}
Network Theory {V, E, T, C, W, F}
The info-comp core is at a HIGHER abstraction level than information theory. Each IT primitive is a SPECIFIC CONFIGURATION of the info-comp core meta-primitives:
- Σ = St at discrete setting
- P = Str at probabilistic setting
- C = part of Tr at stochastic setting
- K (now Ke+Kd) = part of Tr at structured-transformation setting
- μ = Me at entropy setting
- Γ = Co at capacity-bounds setting
The previous session's analysis grabbed information theory and treated it AS the info-comp core. This was an error — they're at different abstraction levels.
2. The key structural question: Tr bundles too much
2.1 What Tr subsumes
The info-comp core's Transformation (Tr) primitive absorbs ALL of these across the four domains:
| Domain | What Tr absorbs | Character |
|---|---|---|
| IT | C (channel) — stochastic transmission | TRANSFER — moving states through a medium |
| IT | K (code) — encoder/decoder | EVALUATION — interpreting/translating states |
| CC | A (algorithm) — computational procedure | EVALUATION — deterministic computation |
| DS | φ (flow) — dynamical evolution | TRANSFER — states evolving over time |
| NT | E (edges) — connections between nodes | TRANSFER — paths through topology |
| NT | F (flow) — quantities moving through network | TRANSFER — directed movement |
2.2 Transfer vs evaluation
Looking at what Tr absorbs, there's a clear split:
Transfer operations: C (channel), φ (flow), E (edges), F (flow) — states MOVING through media or topology. The medium may introduce noise but the operation is about MOVEMENT.
Evaluation operations: K (code — especially Kd decoder), A (algorithm) — states being INTERPRETED or COMPUTED. The operation is about MEANING or FUNCTION, not movement.
This is the SAME split that biology and entity system already make:
- Biology: T (transcription) = transfer, R (ribosome) = evaluation
- Entity system: M (emit) = transfer, X (dispatch) = evaluation
2.3 Would we have caught this from the original derivation?
The methodology.md §5.2 already noted:
Both substrates split the Transformation (Tr) role into transfer + evaluation.
So the structural evidence was VISIBLE at the time of the original analysis. Both biology and entity system explicitly separate transfer from evaluation as independent primitives. The info-comp core bundled them back together because:
- The info-comp core was derived from IT, CC, DS, NT — where the split is less prominent
- In IT: C (channel) and K (code) are both classified as "transformation" operations
- In DS: there's no clear evaluator — the dynamics IS the transformation (φ)
- In CC: the algorithm (A) is both transfer and evaluation bundled together
- In NT: edges and flows are transfers; no clear evaluation
The split is prominent in INFORMATION-PROCESSING substrates (biology, entity system, cognition) but less prominent in the original four info-comp domains (IT, CC, DS, NT).
2.4 Why the cognitive substrate forced the issue
The cognitive substrate analysis revealed that the evaluator can be SPLIT in a single system — formal mode (Kd3-4) vs linguistic mode (Kd1-2). This made the evaluator distinction URGENT because:
- You can have the same encoding (language) with different evaluation modes
- The reliability of the whole system depends on which evaluator mode is active
- Shannon's framework can't express this because it assumes deterministic decoders
Without cognitive substrates in the picture, biology and entity system both have deterministic evaluators (Kd4) — the distinction exists but doesn't cause problems. It's like a dimension that's always at the same value — you don't notice it until something varies along it.
The cognitive substrate VARIES along the evaluator dimension (Kd1-4 depending on content type). That's what made the bundled K → separate Ke+Kd revision necessary.
3. Does the info-comp core need the same revision?
3.1 The case for splitting Tr
The info-comp core's Tr bundles transfer and evaluation. Both biology and entity system split them. Cognitive substrates make the split critical. The split should propagate UP to the info-comp core level because:
- The info-comp core is supposed to capture what ALL information-processing systems share
- ALL information-processing substrates (biology, entity system, cognition) distinguish transfer from evaluation
- The distinction has structural consequences (evaluator determinism determines system reliability)
If the info-comp core doesn't split Tr, it can't express the evaluator distinction — and that distinction is the most important structural variable we've found.
3.2 The proposed split
Transformation (Tr) splits into:
Transfer (Tf) — How states MOVE through media or topology. Channels, flows, transmission, transcription, emit. The medium may introduce noise but the operation is about movement/propagation.
Subsumes:
- IT: C (channel)
- DS: φ (flow)
- NT: E (edges), F (flow)
- Biology: T (transcription, in the movement sense)
- Entity system: M (emit — observable data propagation)
Evaluation (Ev) — How states are INTERPRETED, decoded, or computed. The operation that translates encoding into function or meaning.
Subsumes:
- IT: K (code), now Ke+Kd (encoder+decoder)
- CC: A (algorithm)
- Biology: R (ribosome)
- Entity system: X (dispatch)
- Cognition: Sy (symbolization, formal/linguistic split)
3.3 What about DS and NT evaluators?
Dynamical systems and network theory don't have obvious evaluators:
- DS: The dynamics φ IS the system — there's no separate mechanism interpreting states. The "evaluator" is the dynamical law itself, and it's deterministic (Ev = Kd4 — always the same flow for the same state).
- NT: Network topology defines propagation — edges and paths are structural, not interpretive. The "evaluator" is the graph structure, also deterministic.
So DS and NT have IMPLICITLY deterministic evaluators — the dynamics/topology IS evaluation, and it's always deterministic. The evaluator is only a separate, visible concern in domains where it can be NON-deterministic (cognition, culture, AI).
This means: Ev can be at level 0 (absent/degenerate) for DS and NT. It's still a valid primitive — it just happens to be at a low or ambient level in those domains. This is like how the entity system's P (peer) is at P0 for single-peer implementations — the primitive exists, it's just not active.
3.4 The revised info-comp core
| # | Primitive | What it is | Subsumes |
|---|---|---|---|
| 1 | State (St) | What exists — configurations, symbols, problems, nodes | Σ, Π, Ω, V |
| 2 | Structure (Str) | How states relate — distributions, topology, metrics | P, T(NT), Ω-structure |
| 3 | Transfer (Tf) | How states move — channels, flows, transmission | C, φ, E, F |
| 4 | Evaluation (Ev) | How states are interpreted — decoding, computation, dispatch | K(→Ke+Kd), A, ribosome, dispatch |
| 5 | Measure (Me) | How quantities are computed — entropy, complexity, invariants | μ, R, I, centrality |
| 6 | Constraint (Co) | What bounds apply — capacity, complexity classes, conservation | Γ, R-bounds, conservation |
| 7 | Parameter (Pa) | What's externally varied — noise, input size, control parameters | noise, n, λ, W |
7 primitives. Matching the information theory revision (6→7 by splitting K) and the SSA revision (6→7 by separating Vr from En).
3.5 Core triad — revised
Previously {St, Tr, Me}. With Tr split:
Candidate: {St, Tf, Ev} — "What is a dynamic information system?" States that are transferred and evaluated.
- St-Tf: states move through channels/flows ✓
- St-Ev: states are interpreted/decoded ✓
- Tf-Ev: transferred states are then evaluated (the communication chain) ✓
This works. And it parallels:
- Info theory: {Σ, C, K} → now {Σ, C, Ke+Kd} — symbols transmitted through channels, encoded and decoded
- Entity system: {E, M, X} → entities emitted and dispatched
- Biology: {G, T, R} → genome transcribed and translated
Alternative: {St, Ev, Me} — "What is a measurable evaluated system?" States that are interpreted and measured. This would keep closer to the original {St, Tr, Me} by replacing Tr with its more important half (Ev).
The better test: which triad defines "what the info-comp core IS?" The answer is probably {St, Tf, Ev} — because the TRANSFER+EVALUATION chain is the core operation of information processing. Measure (Me) is important but it's about QUANTIFYING the system, not about the system itself.
3.6 Mapping to information theory (checking consistency)
| Info-comp core (7) | Information theory (7) | Quality |
|---|---|---|
| St | Σ (Symbol) | Direct — states ARE symbols |
| Str | P (Distribution) | Direct — structure IS statistical organization |
| Tf | C (Channel) | Direct — transfer IS channel transmission |
| Ev | Ke+Kd (Encoder+Decoder) | Good — evaluation IS encoding+decoding. The IT level further splits Ev into encoder and decoder. |
| Me | μ (Measure) | Direct — measure IS entropy/information quantities |
| Co | Γ (Constraint) | Direct — constraints ARE bounds and rates |
| Pa | — (no IT counterpart) | Weak — parameters don't have a clear IT primitive |
The mapping is clean EXCEPT for Pa. Information theory doesn't have an explicit Parameter primitive — noise levels, code parameters, etc. are SETTINGS of other primitives (C has a noise parameter, K has code parameters). This raises the question: is Pa genuinely irreducible at the info-comp core level, or is it a property of other primitives?
3.7 Pa (Parameter) under scrutiny
Pa subsumes: noise level (IT), input size (CC), bifurcation parameter λ (DS), edge weights (NT).
These are EXTERNAL INPUTS that change system behavior without changing system structure. But are they a separate PRIMITIVE, or are they partial-level settings of other primitives?
- Noise level: a SETTING of the channel C — C's partial levels already capture noise characteristics
- Input size: a SETTING of the problem Π — how big the input is
- λ (control parameter): a SETTING of the dynamics φ — what bifurcation regime you're in
- Edge weights: a SETTING of the network topology E — how strong connections are
These all look like PROPERTIES of other primitives rather than independent primitives. Pa might be reducible to partial-level parameters within St, Str, Tf, etc.
Counter-argument: Parameters are what you VARY in experiments. They're the independent variables. They have their own structure (parameter space topology, bifurcation diagrams). They're not the same as partial levels — a partial level is about HOW MUCH of a primitive is present, while a parameter is about WHICH CONFIGURATION of a primitive is active.
This is genuinely unclear. Pa might survive or might be absorbed. Let me flag it and move on.
4. Checking the mapping to biology and entity system
4.1 Biology mapping (revised info-comp core)
| Info-comp (7) | Biology | Quality |
|---|---|---|
| St | G (Genome) | Good — genetic state |
| Str | Reg (Regulation) | Moderate — regulatory structure organizes genome expression |
| Tf | T (Transcription) | Good — RNA transfer |
| Ev | R (Ribosome) | Direct — deterministic evaluation (Kd4) |
| Me | — (implicit) | Weak — biology doesn't have an explicit "measure" primitive |
| Co | Mem (Membrane) | Moderate — membrane as boundary/constraint |
| Pa | — (implicit) | Weak — environmental parameters, but not explicit |
Issues: Me and Pa don't have clean biology counterparts. This suggests either:
- Me and Pa are NOT universal across all information-processing domains (they're specific to the mathematical domains IT/CC/DS/NT)
- Biology DOES have measure and parameter equivalents but we haven't identified them
Option 1 seems more likely — biology's primitives were identified through biological literature, not through info-comp mapping. Biology has 6 primitives {G, T, R, P, Reg, Mem} that cover its structure. Not everything in the info-comp core needs a biology counterpart — the info-comp core abstracts a DIFFERENT set of domains.
4.2 Entity system mapping (revised info-comp core)
| Info-comp (7) | Entity system | Quality |
|---|---|---|
| St | E (Entity) | Direct — typed data as state |
| Str | T (Tree) | Good — tree namespace as structure |
| Tf | M (Emit) | Good — observable mutation as transfer |
| Ev | X (Execution) | Direct — handler dispatch as evaluation (Kd4) |
| Me | I (Identity) | Moderate — content hash as measure of identity |
| Co | — (implicit) | Weak — constraints are designed, not explicit primitive |
| Pa | P (Peer) | Moderate — peer as external context/parameter |
Better mapping than biology but still imperfect. I → Me is a stretch (identity isn't really "measure" in the info-comp sense). P → Pa is a stretch (peer is a full primitive with capabilities, not just a parameter).
4.3 What the imperfect mappings tell us
The info-comp core doesn't perfectly abstract biology or entity system. This makes sense:
- The info-comp core was derived from IT, CC, DS, NT — MATHEMATICAL domains
- Biology and entity system are PHYSICAL/COMPUTATIONAL substrates
- The SSA (Situated Substrate Architecture) was derived to abstract biology + entity system + cognition
- The info-comp core and SSA are COMPLEMENTARY abstractions, not the same thing
The info-comp core abstracts the INTERNAL INFORMATION-PROCESSING structure. The SSA abstracts the EXTERNAL SITUATED ARCHITECTURE. They meet at the substrate level but from different angles:
- Info-comp sees: how information is structured, transferred, evaluated, measured, constrained
- SSA sees: how the substrate is encoded, evaluated, mechanized, surfaced, contextualized, communalized, selected
The Ev → Vr (evaluator) mapping IS clean across both frameworks. This is the most important mapping — the evaluator/decoder distinction shows up in BOTH abstractions.
5. What needs to happen
5.1 The info-comp core should split Tr into Tf + Ev
The case is strong:
- Both biology and entity system already split transfer from evaluation
- The cognitive substrate makes the evaluator distinction critical
- The information theory revision (K→Ke+Kd) is the same split at a lower abstraction level
- Ev maps cleanly to SSA's Vr (evaluator)
5.2 Pa needs scrutiny
Parameter might be reducible to partial-level settings of other primitives. Needs the 3/3b iteration test — can we absorb Pa without losing analytical capability?
5.3 The info-comp core is a different domain from information theory
They should not be confused. The info-comp core abstracts IT + CC + DS + NT. Information theory IS one of the four. The relationship is configuration: IT is info-comp core at specific settings.
5.4 The info-comp core and SSA are complementary
They abstract the same substrates from different angles. The key shared finding: the evaluator distinction (Ev in info-comp, Vr in SSA, Kd in IT). This is the structural invariant that shows up at every abstraction level.
5.5 Full 12-step analysis needed
The revised info-comp core at 7 (or 6 if Pa absorbs) needs the full treatment: dependencies, pairs, sublattice, compositions, manifestation landscape. The Pa question specifically needs the 3/3b iteration to resolve.
6. Summary
The info-comp core {St, Str, Tr, Me, Co, Pa} and information theory {Σ, P, C, K, μ, Γ} are DIFFERENT DOMAINS at different abstraction levels. The previous session's analysis confused them.
The info-comp core's Transformation (Tr) bundles transfer and evaluation — the same bundling that information theory's Code (K) had before it was split into Ke + Kd. Both biology and entity system already split this distinction as separate primitives (T/R and M/X respectively).
The evaluator distinction COULD have been caught from the original substrate mappings (both biology and entity system split Tr) but WASN'T because:
- The info-comp core was derived from IT, CC, DS, NT where the split is less visible
- In DS and NT, the evaluator is implicitly deterministic (ambient)
- The cognitive substrate analysis made it urgent by varying along the evaluator dimension (formal vs linguistic mode)
The proposed revision: Tr → Tf (Transfer) + Ev (Evaluation), giving 7 info-comp core primitives. Pa (Parameter) may be reducible and needs 3/3b testing. A full 12-step analysis of the revised info-comp core is needed.
7. The abstract information substrate — a different domain
7.1 The distinction
There are THREE different analytical objects that have been conflated:
-
Information theory {Σ, P, C, Ke, Kd, μ, Γ} — a specific mathematical domain. One of IT/CC/DS/NT.
-
Info-comp core {St, Str, Tr(→Tf+Ev), Me, Co, Pa} — the abstraction of IT/CC/DS/NT. What all four mathematical info-comp domains share.
-
Abstract information substrate — the abstraction of biology, entity system, cognition. What all three CONCRETE information substrates share. This is what needs to be derived.
The previous session's analysis used information theory (#1) in the role of the abstract substrate (#3). This was an error — they're at different abstraction levels and abstract different domains.
The abstract information substrate should be derived the same way the abstract surface was derived — by looking at 3+ concrete instances at the same type-position and finding what they share.
7.2 Derivation from three concrete substrates
The three analyzed information substrates:
| Primitive role | Biology | Entity system | Cognition |
|---|---|---|---|
| Encoding core | G (genome) | E (entity) + I (identity) | Rp (representation) |
| Encoding structure | T (transcription) | T (tree) | Ct (categorization) |
| Evaluator | R (ribosome) | X (execution/dispatch) | Sy (symbolization) |
| Direction | Reg (regulation) | M (emit — triggers evaluation) | Ev (evaluation — what matters) |
| Output | P (protein — functional product) | M (emit — observable change) | As (association — connections) |
| Boundary | Mem (membrane) | P (peer — capability boundary) | Sq (sequence — temporal boundary) |
Six abstract roles recur across all three substrates. Each concrete substrate fills them with different primitives, but the ROLES are consistent.
7.3 The abstract information substrate primitives
| # | Primitive | What it does | Why it's irreducible |
|---|---|---|---|
| 1 | Encoding (En) | Stores/represents information in a specific medium | Without encoding, no information exists in the system |
| 2 | Structure (St) | Organizes encoding — how encoded information is arranged | Without structure, encoding is unorganized — no addressing, no hierarchy |
| 3 | Evaluator (Ev) | Translates encoding into function in a different medium | Without evaluation, encoding is inert — stored but non-functional |
| 4 | Direction (Dr) | Guides what gets evaluated — regulatory control | Without direction, evaluation is undirected — everything evaluated equally |
| 5 | Output (Op) | Functional product of evaluation — what the system produces | Without output, evaluation is internal — no observable effect |
| 6 | Boundary (Bd) | Defines system scope — what's inside vs outside | Without boundary, no distinct system — merges with environment |
7.4 Core triad: {En, St, Ev}
"What is an information substrate?" → Information is ENCODED (En), ORGANIZED (St), and EVALUATED (Ev) — translated from encoding into function.
All three pairs:
- En-St: encoding organized by structure (genome in chromosomes, entities in trees, representations in categories) ✓
- En-Ev: encoding translated to function (codons → proteins, typed data → computation, symbols → meaning) ✓ — THE genesis transition
- St-Ev: structural organization determines what gets evaluated and how (gene expression patterns, tree dispatch routes, categorical associations) ✓
This maps to the concrete core triads:
- Biology: {G, T, R} = {En, St, Ev}
- Entity system: {E+I, T, X} = {En, St, Ev}
- Cognition: {Rp, Ct, Sy} = {En, St, Ev}
7.5 The evaluator IS the critical variable
The abstract information substrate confirms: the evaluator (Ev) is where substrates differentiate.
| Substrate | Ev level | Consequence |
|---|---|---|
| Biology | Ev4 (ribosome — deterministic molecular machine) | Hard substrate — same codon always → same amino acid |
| Entity system | Ev4 (dispatch — deterministic type-checked routing) | Hard substrate — same typed data always → same handler |
| Cognition (formal) | Ev3-4 (proof checking — near-deterministic) | Hard mode — same proof steps always → same validity |
| Cognition (linguistic) | Ev1-2 (interpretation — variable) | Soft mode — same text → different meanings for different readers |
The Ev3→Ev4 transition IS the hard-substrate gate. This is visible at the abstract level without reference to information theory or Shannon.
7.6 How this fits the invariant topology
The invariant topology discovered in the biology analysis:
En → Ev → Mc → Sf → Cm ← Se
↕ ↕
Cx ←→ Cm
The abstract information substrate {En, St, Ev, Dr, Op, Bd} sits at the En + Ev position in the SSA. Specifically:
- En and St together ARE the SSA's En (Encoding) — the abstract substrate's encoding + structure = the SSA's encoding node
- Ev IS the SSA's Vr (Evaluator) — direct one-to-one
- Dr, Op, Bd are the non-core primitives that EXPAND through Mc (Mechanism) into the surface Sf
The SSA's {En, Vr, Mc, Sf, Cx, Cm, Se} is the TOPOLOGY. The abstract information substrate {En, St, Ev, Dr, Op, Bd} is the CONTENT of the substrate nodes in that topology.
7.7 How this relates to the info-comp core
The info-comp core {St, Str, Tf, Ev, Me, Co, Pa} and the abstract information substrate {En, St, Ev, Dr, Op, Bd} are DIFFERENT abstractions of overlapping systems:
| Abstract info substrate | Info-comp core | Overlap? |
|---|---|---|
| En (encoding) | St (state) | Related — encoding IS state in a medium |
| St (structure) | Str (structure) | Close — both about how things are organized |
| Ev (evaluator) | Ev (evaluation) | Direct match — same concept |
| Dr (direction) | — | No counterpart — direction is architectural, not mathematical |
| Op (output) | Tf (transfer) | Related — output IS transferred result |
| Bd (boundary) | — | No counterpart — boundary is architectural |
| — | Me (measure) | No substrate counterpart — measure is mathematical |
| — | Co (constraint) | No substrate counterpart — constraint is mathematical |
| — | Pa (parameter) | No substrate counterpart — parameter is mathematical |
What they share: encoding/state, structure, and evaluator. These are the UNIVERSAL concepts that appear in BOTH mathematical and architectural views of information processing.
What they don't share: the info-comp core has mathematical primitives (measure, constraint, parameter) that don't appear in the architectural substrate view. The abstract substrate has architectural primitives (direction, output, boundary) that don't appear in the mathematical info-comp view.
The EVALUATOR (Ev) is the bridge — it appears in both because evaluator determinism is simultaneously a mathematical property (decoder reliability) and an architectural property (translation mechanism fidelity).
7.8 The relationship between them
The abstract information substrate and info-comp core are connected by a role-identification edge — both describe information processing, from complementary perspectives:
- Info-comp core: the MATHEMATICAL view — what are the information-theoretic properties?
- Abstract info substrate: the ARCHITECTURAL view — what are the structural components?
A specific information substrate (biology, entity system, cognition) has positions in BOTH lattices:
Biology position in abstract info substrate lattice:
En=Full, St=Full, Ev=4-Full, Dr=Full, Op=Full, Bd=Full
Biology position in info-comp core lattice (as information processing):
St=Full, Str=Full, Tf=4, Ev=4-Full, Me=2, Co=2, Pa=1
The two positions describe the SAME system (biology) from different analytical perspectives. The role-identification edge connects them — showing which abstract substrate roles correspond to which info-comp properties.
7.9 What this means for the analysis
We now have THREE abstract domain mirrors at the substrate/surface/ecosystem levels of the invariant topology:
| Level | Abstract domain | Primitives | Derived from |
|---|---|---|---|
| Substrate | Abstract information substrate | 6: {En, St, Ev, Dr, Op, Bd} | Biology + entity system + cognition |
| Surface | Abstract surface | 7+2: {St, Or, Rg, Pr, Ac, Pt, Ex, [Rs], [Gn]} | Organism + app + cognitive arch |
| Ecosystem | Abstract ecosystem | 9: {Pd, Tf, Cy, Dv, In, Rg, Sp, Tp, Ct} | Biological + cultural + (digital) ecosystem |
And the info-comp core sits ALONGSIDE this as a complementary abstraction:
| Abstraction | What it abstracts | Perspective |
|---|---|---|
| Abstract info substrate | Biology, entity system, cognition | ARCHITECTURAL — components and their roles |
| Info-comp core | IT, CC, DS, NT | MATHEMATICAL — information-theoretic properties |
| SSA | The topology connecting substrate → surface → ecosystem | STRUCTURAL — how the architecture connects |
These are three views of the same landscape:
- The abstract substrate tells you WHAT information systems are made of
- The info-comp core tells you HOW information is processed mathematically
- The SSA tells you HOW the pieces connect into situated systems
7.10 Next steps
-
Full 12-step of abstract information substrate {En, St, Ev, Dr, Op, Bd} — dependencies, pairs, sublattice, compositions, manifestation landscape
-
Validate the mappings — check that each concrete substrate's primitives cleanly fill the 6 abstract roles
-
Info-comp core revision — the Tr→Tf+Ev split stands, but this is a SEPARATE domain from the abstract info substrate. Both need analysis, but they're different domains connected by role-identification.
-
Check: does the abstract info substrate have the same tight filter as concrete substrates? If the abstraction is right, the abstract domain should share structural properties with its instances (like the abstract surface shares loose filter with concrete surfaces).