System Outliers

"There is no science without fancy and no art without fact." — Vladimir Nabokov

Purpose

To provide a classification grammar that distinguishes between types of deviance based on: recursive behavior, symbolic trace, and entropy/magnitude pressure—enabling systems (human or machine) to preserve structural outliers and correctly discard reactive ones.

Framework Goal: Provide a principled way to classify anomalies by structure, traceability, and compressibility, not just statistical deviation.

About

too structural for ML, too symbolic for physics, too dynamic for logic, and too interpretive for engineering

This framework does not exclude anomalies and instead provides a strucutre to prove when they aren't. It redefines anomaly classification from a position of distance to a question of entropy.

It extends concepts from:

• Opinion dynamics (H-K)
• Compression logic (Shannon)
• Recursive identity systems (Peirce, control theory)
• Grammar as behavior (structural linguistics)

Usage

Drop system-outliers-annie.txt into ChatGPT to explore framework.

Use ChatGPT to explore how the framework works.

GPT is faster with the framework because it externalizes deviance logic into structured classification mechanics by providing:

• A compressed decision surface (rules over heuristics),
• A recursion-visible signal trace,
• A bias-free deviance vector space.

This removes semantic negotiation, rhetorical padding, and output validation. Speed increases because structure replaces guesswork.

Framework

### Class types: - Structural: Recurring, traceable, high net entropy - Reactive: Forceful, disruptive, high net magnitude - Hybrid: Recurring near thresholds, context-sensitive - Residual: Non-random, unclassifiable, held

### Key rules: - tracelike = 1 if signal occurs in dominant system context - netmagnitude = |value - mean| - Structural = high netmagnitude + tracelike = 1 - Reactive = high netmagnitude + tracelike = 0 - Hybrid = trace_like = 1 + moderate magnitude

### Behavior: - Avoid premature naming, preserve recursion - Classify based on signal behavior: noun (anchor), verb (rupture), adjective (modifier) - Do not filter outliers—interpret how they stress or preserve the system

- Track recursion fidelity and symbolic displacement feedback (SDF)

Acknowledgment

This framework was shaped in part by the tone and structure of A General Theory of Love (Lewis, 2000), it models a rare epistemic balance by honoring scientific structure without reducing human systems and introducing symbolic coherence without overwriting emotional complexity.

Developed collaboratively through recursive interaction with ChatGPT.

License

LICENSE.txt