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Starting from SIR and SIRS models, this project incorporates seasonality and self-regulation effects into disease dynamics, followed by bootstrapping to analyze uncertainty and confidence intervals across the parameter space.
This theory defines a dynamic framework in which systems transition between multiple stability phases while maintaining adaptability and integrity. It supports phase-aware optimization, fault-tolerant response, and robust self-regulation in evolving environments.
This system models adaptive reflection across multiple cognitive layers, enabling agents to restructure their reasoning through a meta-level feedback process. It enhances self-regulation and decision robustness in dynamic environments. 本システムは、複数の認知層にわたる適応的反射をモデル化し、エージェントがメタレベルのフィードバックを通じて推論を再構築できるようにします。これにより、動的環境下での自己調整力と意思決定の堅牢性が向上します。