Dynamic logic of phenomena and cognition

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process is called dynamic logic (DL) of model construction, which mimics processes of the mind and natural evolution. This paper provides a formal description of phenomena dynamic logic (P-DL) and outlines its extension to the cognitive dynamic logic (C-DL). P-DL is presented with its syntactic, reasoning, and semantic parts. Computational complexity issues that motivate this paper are presented using an example of polynomial models.