Reality Drift: Diagnostic Visual Models of System Misalignment and Synthetic Systems

This dataset contains a collection of visual conceptual models used to analyze epistemic degradation, semantic collapse, and recursive symbolic systems in modern cognitive, organizational, and AI-mediated environments.
The images represent formal structural patterns observed in synthetic systems, including cognitive compression, semantic fidelity loss, optimization traps, filter fatigue, performativity drift, and recursive symbolic feedback loops.
The dataset is intended to support research and analysis in systems theory, AI governance, institutional epistemology, and human–machine cognition. The models function as analytical tools for reasoning about how symbolic systems evolve when internal representations increasingly replace external reality as the primary reference point for decision-making.
These models are part of the Reality Drift framework, which describes how systems remain operational while gradually losing alignment with the real-world conditions they were meant to track.
Across domains, the same structural pattern appears: optimization pressure exceeds constraint capacity, leading to compression, representational substitution, and feedback-driven drift.
This version reflects a refined and canonical set of visual models. Earlier iterations are preserved in prior versions of this record.
Part of the Reality Drift framework (2023–2026) by A. Jacobs.