Self-Organizing Information and the Emergence of Structure, Memory, and Observer-Relative Reality

Abstract This paper develops a non-reductive but physically motivated framework in which reality is modeled as an evolving informational process. The central claim is not that the universe literally “computes” in a narrow engineering sense, but that its large-scale and small-scale regularities can be fruitfully analyzed as the persistence, transformation, and selection of stable informational configurations under physical constraints. On this view, structure is not imposed from outside; it emerges from local interactions, energetic constraints, and path dependence. Memory is treated not as a mental add-on but as structural persistence: prior states bias future states by shaping the space of accessible configurations. The paper further argues that observer-relative temporal experience can be modeled as a ratio between internal dynamical change and external environmental change, yielding a testable family of predictions. Finally, the paper distinguishes metaphorical language about “learning” from operational claims about selection and stability, and it proposes empirical routes for evaluating the model in psychological, network-analytic, and computational settings.