Dynamic Category

Dynamic categories are a mathematical framework designed to model systems that adapt and evolve over time, mirroring the behavior of natural and artificial systems. Current research focuses on applying this framework to understand complex phenomena like deep learning (viewed as a dynamic monoidal category) and prediction markets (as a dynamic operad), providing a unified theoretical lens for diverse adaptive processes. This approach offers a powerful tool for analyzing and potentially designing more robust and adaptable systems across various scientific and engineering domains.