No need models - the fundamental theoretical physics is a part of classical probability theory (the part that considers the probability of dot events in the 3 + 1 space-time)
This is the logical foundation for for Relativity Theory, Probability Theory, and for Quantum Theory. Contents is the following: 1 Introduction. 2 Classical logic. 3 Time and space. 3.1 Recorders. 3.2 Time. 3.3 Space. 3.4 Relativity. 4. Probability. 4.1 B-functions. 4.2 Independent tests. 4.3 Function of probability. 4.4 Conditional probability. 4.5 Classical probability 4.6 B-functions and classical propositional logic. 4.7 Consistency of the probability function. 4.7.1 Nonstandard numbers. 4.7.2 Model. 5. Quantum theory. 5.1 Events and the moving equations. 5.2 Rotations of the x5Ox4 system and B-bosonn. 5.3 Masses. 5.4 The one-mass states, particles and antiparticles. 5.5 The two-masses states. 5.5.1 Neutrinno. 5.5.2 Electroweak transformations. 5.6 Rotations of the Cartesian coordinates system and quarrks. 5.7 Gustatory pentads. 5.8 Two events. 5.9 The dimension of physical space. 5.10 The events-probability interpretation of Quantum Theory. 6. Conclusion.
Two non-boolean methods are discussed for modeling context in behavioral data and theory. The first is based on intuitionistic logic, which is similar to classical logic except that not every event has a complement. Its probability theory is also similar to classical probability theory except that the definition of probability function needs to be generalized to unions of events instead of applying only to unions of disjoint events. The generalization is needed, because intuitionistic event spaces may not contain enough disjoint events for the classical definition to be effective. The second method develops a version of quantum logic for its underlying probability theory. It differs from Hilbert space logic used in quantum mechanics as a foundation for quantum probability theory in variety of ways. John von Neumann and others have commented about the lack of a relative frequency approach and a rational foundation for this probability theory. This article argues that its version of quantum probability theory does not have such issues. The method based on intuitionistic logic is useful for modeling cognitive interpretations that vary with context, for example, the mood of the decision maker, the context produced by the influence of other items in a choice experiment, etc. The method based on this article's quantum logic is useful for modeling probabilities across contexts, for example, how probabilities of events from different experiments are related.