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Multi-Level Constraint Recursive Realization (MCRR): A Cross-Level Theoretical Framework Based on Meta-Constraints and Recursive Optimization

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Preprints.org
DOI
10.20944/preprints202511.1057.v3

This paper proposes the "Multi-level Constraint Recursive Realization" (MCRR) framework, which seeks to provide a logically unified, first-principles-based meta-theoretical model for understanding the continuity spanning physical systems, life, cognition, and socio-cultural phenomena. Its core thesis is that the very existence of any dissipative structure, which intends to persist over time, implies that it must simultaneously and continuously satisfy three absolute meta-constraints that are logically irreducible to one another: (1) acquiring resources from the environment, (2) optimizing internal processes to reduce the cost of persistence, and (3) maintaining the boundary and structural stability that define it as a unified whole. These constraints constitute the "hard boundaries" of a system's existence; violation of any single constraint leads to the system's dissipation or disintegration. Building upon this foundation, the framework constructs a logical hierarchy of systems, ranging from passive physical structures to active autopoietic systems, further to systems with adaptive behavioral tendencies and internal evaluative minds, and ultimately to institutionalized societies. Each higher level can be viewed as a strategic solution, recursively evolved by the system to cope with environmental complexity, aimed at satisfying the underlying meta-constraints more robustly or efficiently. Specifically, we argue that the essence of mind (encompassing sensation, emotion, and cognition) is a dynamic multi-constraint value-computation and optimization system, whose evolution addresses conflicts among basic behavioral tendencies in complex environments. The framework engages in a deep dialogue with theories such as autopoiesis, life history theory, and active inference, thereby providing an analytical tool and conceptual map designed to integrate, not replace, knowledge from existing disciplines.

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