The Ambiguity Engine (AE): A Conceptual Framework for Human Interpretation and Decision-Making Under Uncertainty

The Ambiguity Engine (AE) is a conceptual framework for how people interpret incomplete information and make decisions without certainty. It proposes that present observations are compared with an accumulated “Codex” of experiences, memories, learned knowledge and interpreted outcomes to form possible meanings, weigh risk and support action.

The paper introduces the Missing Book Problem, Ambiguity Load, Recognition Before Language, Trust as Prediction Reliability, and Awareness as an Exception-Reporting System. AE follows Observer–Mirror Theory (OMT) in a wider framework examining how experience becomes interpreted meaning. It is offered as a conceptual model for discussion and future testing, not as a proven scientific theory.