AIM Framework glossary
Aggregation Gradient
Canonical definition
The monotonic relationship between representational distance from any single Decision Hub and the legibility of the Mimetic Premium. The gradient comprises six rungs: Individual single-moment (Rung 1, where Source Opacity is total), dyadic transaction (Rung 2), Individual longitudinal (Rung 3), small group / network (Rung 4), institutional (Rung 5), and macro-historical (Rung 6). At each higher rung, aggregation across more Decision Hubs and longer time periods suppresses the bounded contributions of A and I and lets the unbounded compounding of M dominate the signal, increasing the legibility of the M-component to third-party analysis equipped with AIM Vocabulary. The gradient does not undo Source Opacity at lower rungs; it identifies the dimension along which M-detection survives the Camouflage Architecture's eight-layer concealment.
Explanation
You cannot tell, in any single moment, why you wanted what you wanted. But step back. Watch yourself across many moments. Watch a relationship across many exchanges. Watch a profession across many years. Watch a society across many decades. As you step back, the bits of motivation that satisfy and cycle (A) and the bits that deepen but do not compound (I) average out, and the one ingredient that never satisfies and always compounds (M) starts to dominate what you can see. The further you step back, the clearer M becomes. Source Opacity stops you from seeing M when you stand on top of yourself; the Aggregation Gradient is what lets you see it when you stand back. Substrate specification. The Aggregation Gradient is the architectural consequence of the Boundedness Asymmetry Principle: A-bounded contributions average out across aggregation rungs (because A-deficits are heterogeneous and individually finite), I-bounded contributions average out (because I-engagement is heterogeneous and individually deepening), and only M-unbounded contributions compound (because PM has no satiation, Axiom 6, and is networked through the Observability Matrix). The gradient is therefore an M-channel-specific compounding effect operating at the population scale, with A and I providing the bounded baseline against which M-compounding becomes detectable.
Epistemic status
Framework-Specific Construct.
Derivation
Related terms
- Source Opacity (Axiom 3)
- Common-Currency Integration (Axiom 2)
- Mimetic Premium (PM)
- Boundedness Asymmetry Principle
- Low-Pass Filter Mechanism
- Macro-Residue Visibility Principle
- Two-Question Diagnostic
- Crisis State Architecture
- Camouflage Architecture
- Per-Transaction Minority Claim