Reality Drift: How Symbolic Systems Lose the Ability to Correct Themselves Beyond Goodhart’s Law and Cybernetic Feedback: Why Recognition Doesn’t Restore Correction

<p><span>Across institutions, technologies, and knowledge environments, systems increasingly remain operational while producing outcomes that feel hollow, performative, or disconnected from reality. Metrics improve even as results degrade, language remains fluent as meaning erodes, and individuals experience cognitive exhaustion and disorientation without obvious error, deception, or collapse. Existing explanations such as corruption, misinformation, misaligned incentives, or information overload capture surface symptoms. However, they fail to identify a unifying structural mechanism.</span></p> <p><span>This paper proposes </span><strong><span>Reality Drift</span></strong><span>, a theory of representational failure in scaled symbolic systems. The core claim is that, as symbolic systems expand and optimize, their representations drift away from reality faster than corrective constraints can bind them. This drift is gradual, non-intentional, and self-reinforcing. Systems continue to function, but meaning detaches. Optimization replaces reference and symbols circulate autonomously. As a result, human cognition adapts at increasing cost.</span></p> <p><span>The paper introduces a five-operator explanatory grammar consisting of drift, constraint, compression, representation, and filtering. Together these describe how the condition emerges and persists across domains. The theory generates testable predictions for institutions, artificial intelligence, and human cognition while providing a structural diagnosis of how meaning degrades under scale.</span></p>