THE OBSERVATION-WINDOW DEFAULT PROJECTION PRINCIPLE (OWDPP) A PROPOSED INTEGRATIVE FRAMEWORK FOR DEFAULT-PROPERTY PROJECTION FROM BOUNDED OBSERVATIONS OF DYNAMIC SYSTEMS THEORETICAL ARCHITECTURE, FORMAL DEFINITIONS, MATHEMATICAL MODEL, PRIMARY CASE S

The Observation-Window Default Projection Principle (OWDPP) is proposed as an integrative framework for studying how an observer or information-processing system may infer a target system's general, default, or enduring properties from a restricted observation window. The framework begins with an organizational case in which a newly appointed employee encounters a company during a period of disruption and may infer that the observed conditions represent the company's normal or enduring characteristics. The same conceptual structure may be relevant to other observation settings, including restricted datasets, sampled signals, sensor streams, and computational state-estimation processes, but these extensions must be validated independently. The proposed mechanism distinguishes the target system's state, the observation medium, the observation window, the local-state estimate, the reference state, the default-projection operator, signed projection error, absolute projection error, and projection persistence. A formal notation is introduced for dynamic systems and incomplete observations. A weighted projection model and a conditional projection-risk model are specified, together with nine testable hypotheses, operational definitions, boundary conditions, alternative explanations, and three complementary research designs. The theoretical architecture contains sixteen principal layers and six cross-cutting analytical modules. It is designed to support conceptual clarification, mathematical formalization, empirical testing, falsification, and carefully bounded generalization. No empirical findings are claimed in this manuscript. The framework's scientific contribution must be assessed through systematic literature review, construct validation, comparative model testing, and independent replication.