This theory proposes a constructive framework that enables the dynamic integration of causal structures from multiple domains. It allows systems to infer, adapt, and evolve reasoning pathways in response to complex causal interrelations, even when those causes arise from disparate or abstract contexts.
本理論は、複数の領域にまたがる因果構造を動的に統合する構成的フレームワークを提案します。これにより、抽象的または異種の因果関係にも対応し、推論経路を進化的に再構成する知的システムが実現可能になります。
- Cross-disciplinary inference in science and medicine.
- Complex event processing and autonomous hypothesis generation.
- AI systems for causal diagnostics and policy simulation.
- Adaptive decision-making under uncertainty using multicausal inference.
- Education platforms that adapt content based on causal learning patterns.
README.md: Overview and application of the theoryLICENSE: Apache License 2.0sections/definition.md: Definition of constructive causal integrationsections/applications.md: Use cases and practical examplessections/architecture.md: Structural components and logical flow