Modeling Field Theory and Logic

The modeling field theory (MFT) has been developed for modeling complex systems including the most challenging one that is the modeling of mind. It has been successful in dealing with the enormous computational complexity of these problems. One of the main ideas behind such success is the matching of 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 an adjusted model. This process is called a dynamic logic (DL) of model construction. It is likely that the dynamic logic mimics a process of a natural evolution. The goal of this paper is the formalization of the dynamic logic using the first order logic and logic model theory. We introduce partial orders on the models with respect to their uncertainty and specificity. These partial orders are represented using Boolean parameters. The theory of monotone Boolean functions is used for search guiding the search in the parameter space