Principlev1
Treat every experience as training data for continuous
Treat every experience as training data for continuous schema calibration, weighting surprising observations more heavily than confirmations, rather than freezing your models and deploying them indefinitely.
Why This Is a Principle
Grounds in Domain-Specific Calibration Development (calibration from feedback), Brain as Hierarchical Prediction Machine (prediction error propagation), and Learning occurs when outcomes differ from predictions, (learning from prediction error). The principle prescribes online learning architecture for personal cognition. It's about deployment strategy for belief systems.