Quantitative nomic truth approximation by revising models and postulates

In my paper, “Models, postulates, and generalized nomic truth approximation” (Kuipers 2016), I have presented the ‘basic’ version of generalized nomic truth approximation, starting from ‘two-sided’ theories. Its main claim is that nomic truth approximation can perfectly be achieved by combining two prima facie opposing views on theories: (1) the traditional (Popperian) view: theories are (models of) postulates that exclude certain possibilities from being realizable, enabling explanation and prediction and (2) the model view: theories are sets of models that claim to (approximately) represent certain realizable possibilities. Nomic truth approximation, i.e. increasing truth-content and decreasing falsitycontent, becomes in this way revising theories by revising their models and/or their postulates in the face of increasing evidence. The basic version of generalized nomic truth approximation is in many respects as simple as possible. Among other things, it is qualitative in the sense that it is purely based on set-theoretic relations. The present paper presents the straightforward quantitative concretization of it. According to the ‘expected success theorem’, based on some probabilistic experimental conditions, greater truthlikeness, or verisimilitude, leads to greater expected empirical success. This enables tentative nomic truth approximation conclusions by abductive reasoning