Choice & Inference

In Modeling of Phenomena and Dynamic Logic of Phenomena, a new paper to appear in Logica Universalis, Boris Kovalerchuk, Leonid Perlovsky and I propose a system to match the level of uncertainty of a problem to a corresponding level of uncertainty of the criterion used to evaluate the model.

Abstract: Modeling a complex phenomenon such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, a problem or some theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models.