MACROSCOPE: A Framework for Information Attractors, Multiscale Information Dynamics, and Materialized Feedback

Macroscope is a conceptual and operational framework for the analysis of large-scale information dynamics through the notion of Information Attractors (IA). The framework integrates synchronization, structural memory, multiscale dynamics, information transport, recursive feedback, background landscapes, inter-attractor interaction, and materialized physical effects within a unified analytical architecture.The model distinguishes between a conceptual core, an operationalized empirical core, and explicitly testable extensions. It includes measurable state variables such as effective participation, synchronization, phase alignment, accumulated structural memory, and state trajectories, while treating hydrodynamic, phase-like, cognitive, and inter-attractor constructs as hypotheses requiring independent empirical validation.This English Reference Edition also includes two stress-test applications: a biological analysis based on ant-colony dynamics and a digital case study of Bitcoin, including a multiscale slow/fast diagnostic framework and an external physical-response test using hash rate.The document is intended as a research framework and hypothesis-generation system rather than as a fully validated physical theory. Its central methodological principle is that new parameters and extensions should be retained only if they provide reproducible explanatory or predictive value beyond simpler baseline models.