Self-Derivative Computationalist Mind Model

Aiming at core challenges in current Artificial General Intelligence (AGI) development, such as value alignment, decision-making black boxes, and autonomous evolution, this paper proposes a novel theoretical framework named the Self-derivative Computationalist Mental Model (SDCMM). Based on the "Cyclic Four-Dimensional Dynamic Model," the framework describes intelligent systems as operating under the interaction of four dimensions—environment, psychological traits, capability structure, and time—through which dynamic value trade-offs and decisions are made via the "Rational Priority Algorithm" and the "Equity Balance System." Ultimately, actions are generated by the "Board Decision-Making Mechanism," while the "Self-derivative Engine" enables safe and controllable self-iteration. This paper elaborates on the mathematical representations, dynamic interaction mechanisms, and inherent learning logic of each core component of the model, demonstrating its operational process through case studies on individual decision-making and organizational transformation. The SDCMM not only provides a systematic top-level design blueprint for building AGI with long-term consistency, transparency, and ethical sensitivity but also offers a unified cross-scale computationalist perspective for understanding human and organizational intelligence and analyzing their decision-making mechanisms. Representing a philosophy of "top-level design" for constructing intelligence, SDCMM introduces a new paradigm. While it may not offer ready-made "building blocks" to solve all problems, it draws an indispensable "map" that points to the directions and critical junctures essential for guiding intelligence from fragile specialization toward robust generality.