rogue-variable-cognition

name	rogue-variable-cognition
description	Rogue Variable Theory (RVT) for quantum-compatible cognition modeling. Formalizes pre-event cognitive states as structured Rogue Variables embedded in graph Hilbert space via Mirrored Personal Graph (MPG). Includes Rosetta Stone Layer for cross-user latent space alignment. Use when: (1) modeling pre-decision cognitive states, (2) analyzing ambiguity and contextual tension in cognition, (3) building cross-user representation alignment systems, (4) implementing quantum-consistent information-theoretic cognition frameworks. Activation: rogue variable theory, mirrored personal graph, rosetta stone layer, pre-event cognition, cognitive modeling, decision analysis, latent space alignment.
metadata	{"arxiv_id":"2601.00466","published":"2026-01-01","authors":"Jacek Malecki, Alexander Mathiesen-Ohman","tags":["cognition","quantum","graph-hilbert","rosetta-stone","decision-making","alignment"]}

Rogue Variable Theory: Pre-Event Cognition Framework

Core Concept

Many consequential cognitive dynamics occur before events become explicit: before decisions finalize, emotions label, or meanings stabilize. RVT formalizes these as Rogue Variables — structured, pre-event cognitive configurations that influence outcomes while remaining unresolved.

Architecture

1. Mirrored Personal Graph (MPG)

• Nodes represent cognitive elements (beliefs, perceptions, memories)
• Edges represent relationships (support, conflict, uncertainty)
• Each node/edge has time-indexed metrics
• Embedded into fixed graph Hilbert space

2. Quantum MPG State (QMS)

• Normalized state constructed from node and edge metrics under context
• Hamiltonian dynamics derived from graph couplings
• State evolution models cognitive dynamics

3. Rogue Operator

• Error-weighted operator whose principal eigenvectors identify rogue factor directions
• Candidate Rogue Variable segments = eigenvectors with largest eigenvalues
• Reveals unresolved cognitive configurations

4. Rosetta Stone Layer (RSL)

• Maps user-specific latent factor coordinates into shared reference Hilbert space
• Enables cross-user comparison without explicit node alignment
• Critical for multi-user applications

Methodology

Step 1: Construct MPG

• Identify cognitive elements as graph nodes
• Define relationships as edges with weights
• Time-index all metrics

Step 2: Embed in Hilbert Space

• Map graph to fixed-dimension Hilbert space
• Construct normalized QMS from metrics
• Define Hamiltonian from graph structure

Step 3: Compute Rogue Operator

• Build error-weighted operator
• Extract principal eigenvectors
• Identify rogue variable candidates

Step 4: Apply Rosetta Stone Layer

• Map personal coordinates to shared space
• Enable cross-user aggregation
• Compare without node-level alignment

Implementation Notes

• Fully implementable on classical systems
• Does NOT assume physical quantum processes
• "Collapse" = informational decoherence under interaction
• Hamiltonian can be derived from graph Laplacian or adjacency

Pitfalls

• Graph size: Large MPG → large Hilbert space → computational cost
• Context dependency: QMS depends on context; define context clearly
• Eigenvalue degeneracy: Multiple similar eigenvalues → ambiguous rogue directions
• RSL calibration: Shared space quality depends on mapping function choice

Applications

• Decision support systems
• Emotional state tracking
• Cross-user preference alignment
• Ambiguity quantification in NLP
• Cognitive bias detection