Compression Ecology
A framework for understanding how adaptive systems produce compressible descriptions of emergent behavior. The core idea: separate what changes frequently from what changes rarely. Put stable foundations at the bottom, let volatile exploration happen on top.
Key Concepts
Entropy stratification — layers of a system ordered by how much they change. Low-entropy foundations (values, methods) support high-entropy exploration (hypotheses, raw observations).
Logic vs. belief boundary — within what we know, we use lossless compression (logic). Outside that space, we use lossy compression (belief, narrative). The boundary expands as we learn.
Motivation vectors — scaffolding that enables [[Predictive Attention]] outside the known space. Coordinates might be pragmatic/earthly vs. ideal/heavenly success.
Connections
This connects to [[Entropy-Aware Systems]] design — the same pattern applies to software architecture, knowledge management, and research infrastructure.
The logic/belief boundary relates to Nassim Taleb's work on probability and ergodicity — average-based thinking fails precisely when you cross out of the logic region.
Forms
- Essay — readable essay format
- Legacy paper — original academic format
- Formal treatment — mathematical framework