“The Theory of Cognitive Algorithms: Integrating Deduction, Induction, and Dialectics” is a methodology that integrates four bodies of thought — Wang Yangming’s xinxue (reaching innate moral knowledge + the unity of knowledge and action), Zhu Xi’s lixue (the investigation of things and exhaustion of principle), convolutional neural networks (pattern recognition in deep learning), and combinatorial innovation (W. Brian Arthur, The Nature of Technology) — into a single meta-algorithm: cognition = pattern recognition + combinatorial innovation + practical verification. It is the methodological master-framework for researching finance, history, and civilization — first identify existing patterns, then combine them into a new framework, then iterate through practical verification. This entry is the hub node for the system’s cognitive methodology; organization and extensions are placed at the end.
The Framework As It Stands
This section is organized from the compiled research draft: the original framework’s structure, terminology, and key formulations are preserved, including editorial bridging and external factual annotations; diagrams are drawn by the compiler following the original text’s structure.
I. Zhu Xi’s investigation of things and exhaustion of principle = pattern recognition (external seeking). “All things and affairs have their principles” = every thing has an inherent pattern; “exhausting principle” = exhausting these patterns. Zhu Xi (1130–1200) stated the essence of “cognition = pattern recognition” approximately 800 years before modern cognitive science.
II. Wang Yangming’s reaching innate moral knowledge = the innateness of the inner algorithm (internal seeking). Wang Yangming (1472–1529) failed to investigate the bamboo for seven days and nights (ca. 1492), and came to recognize the limits of external investigation → proposed “humans possess innate moral knowledge at birth” (i.e., a congenital cognitive algorithm) → turned to internal seeking (the awakening at Longchang, 1508). This is fully isomorphic with modern cognitive science’s “congenital network architecture + post-training data.”
III. Wang Yangming’s unity of knowledge and action = the iterative process of practical verification. Innate moral knowledge (the congenital algorithm) cannot remain in the mind; it must be verified and corrected through practice — this is the “forward inference + backpropagation” process of convolutional neural networks.
IV. Convolutional neural networks = the scientific realization of modern pattern recognition. Multi-layer neural networks learn to recognize patterns in images, speech, and text through gradient descent; the bottom layers learn edges, the middle layers local features, the top layers the whole — this is Zhu Xi’s “exhausting principle layer by layer” realized in silicon. Historical anchors: 1980 Fukushima Kunihiko’s Neocognitron; 1989 LeCun’s LeNet; 2012 AlexNet (ImageNet breakthrough).
V. Combinatorial innovation = the sole mechanism by which new cognition is born. New technology = recombination of existing technologies (e.g., iPhone = touchscreen + GPS + camera + phone); new cognition = recombination of existing cognition (W. Brian Arthur, The Nature of Technology, 2009). Judgment rule: every “inspiration” is the brain’s background recombination of existing patterns; studying “innovation” requires studying “existing elements + methods of combination,” not studying “inspiration.”
VI. Integration of four sources = the meta-algorithm. Wang Yangming (internal seeking) + Zhu Xi (external seeking) + convolutional neural networks (scientific model) + combinatorial innovation (mechanism) integrated into “cognition = pattern recognition + combinatorial innovation + practical verification.” Three-step meta-algorithm: identify existing patterns (reading) → combine new frameworks (writing) → practical verification (predictive testing).
VII. East-West cognitive theories are isomorphic + universally applicable. Zhu Xi’s investigation of things and exhaustion of principle (external seeking) + Wang Yangming’s reaching innate moral knowledge (internal seeking) = modern cognitive science’s dual process of “external data + congenital algorithm”; convolutional network training (external data) + network architecture (congenital algorithm) = Wang Yangming + Zhu Xi integrated. Judgment rule: Eastern philosophy is not obsolete — it expressed modern cognitive science in classical Chinese. The cognition of any complex problem (finance, politics, history, culture) can proceed in three steps: “identify existing patterns → combine new framework → practical verification.”
Compiler’s Perspective
This section is the Compiler’s Perspective: coordinates and relationships of this entry within the larger system, distinguished from the framework body in the preceding section.
- Coordinates:
Fa (Method)×Its Place in the Whole, and this is a meta-entry — the various topics in this system (financial mechanisms, civilization history, Yi studies) are all instances of this meta-algorithm running in specific domains; system-level propositions are concentrated in this single hub. - Relationship with the philosophical Fa layer: This framework brings three cognitive logics — deduction (formal inference), induction (pattern recognition), and dialectics (combination + verification) — into a single meta-algorithm. The formal necessity of the deductive branch appears in Aristotelian Deductive Logic: The Syllogism; the recursive structure of “exhausting principle layer by layer” can be read alongside Hegel’s Doctrine of Essence: Logical Layering’s layered generation; the “unity of knowledge and action” iteration and the “thesis-antithesis-synthesis → sublation” of Dialectics as Meta-Algorithm both belong to dynamic cognition; the Eastern cognitive tradition that Zhu Xi and Wang Yangming carry forward connects to I Ching Dialectics: The Dual Axes of Timing and Position; the operation of combinatorial innovation and pattern recognition in the economic domain appears in Economic Network Science and Economic Complexity.
- Its Place in the Whole: Completing one round of the three-step meta-algorithm (identify existing patterns → combine new framework → practical verification) does not return to the starting point — the post-verification model carries its corrections into the next round of pattern recognition. This structure connects at the level of foundational orientation to the philosophical basis of thought frameworks: spiral guidance and the negation of negation: each round is the negation of the negation of the previous framework. The specific error in the old approach: treating Wang Yangming’s seven days and nights investigating the bamboo as the act of cognition — fixating on a single object by brute force, expecting that external investigation would directly yield truth, without activating verification and iteration. The sequence in this framework is what Wang Yangming arrived at only after the bamboo investigation failed: first acknowledge the congenital algorithm (innate moral knowledge), then use action as backpropagation-style correction. A statement that only someone who has worked through this four-source comparison could write: Zhu Xi had external seeking but no verification; Wang Yangming had verification but no combinatorial mechanism; convolutional neural networks had the algorithm but no cross-domain combination; Arthur had combination but no practical feedback loop — “cognition = pattern recognition + combinatorial innovation + practical verification” is the result of assembling four incomplete pieces into a whole; no single source alone can yield the full formula.
See Also
- Hegel’s Doctrine of Essence: Logical Layering
- I Ching Dialectics: The Dual Axes of Timing and Position
- Aristotelian Deductive Logic: The Syllogism
- Dialectics as Meta-Algorithm
- Economic Complexity
Sources
- Internal anchor: compiled draft z-0043 · collected July 2026.
- Zhu Xi, Collected Annotations on the Four Books; Wang Yangming, Instructions for Practical Living (Chuanxilu), Inquiry on the Great Learning.
- W. Brian Arthur, The Nature of Technology (2009).
- LeCun, Bengio & Hinton, “Deep Learning,” Nature (2015); Fukushima Kunihiko, Neocognitron (1980); LeCun, LeNet (1989); AlexNet (2012).