Philosophical models

Johannes Hunger (Chapter 7) takes on another of the standard topics in the philosophy of science, explanation. Hunger examines, in detail, various ways that chemists explain and predict the structural properties of molecules. We learn about ab initio methods, empirical force field models and neural network models, each of which have been used to explain and predict molecular structure. And we learn that none of these approaches can be subsumed under either hypothetico-deductive or causal models of explanation. Either chemistry does not offer proper explanations (the normative option) or our philosophical models for explanation are inadequate to cover explanation in chemistry (the descriptive option). Hunger takes the descriptive option and sketches a more pragmatic approach to the explanation that develops Bas van Fraassen s approach to explanation for chemistry. Once again, we find that the philosophy of science has much to learn from the philosophy of chemistry. 

A general treatment of Cicero s philosophical models is A. A. Long, Cicero s Plato and Aristotle, in Cicero the Philosopher. More specifically related to On the Commonwealth are R. Sharpies, Cicero s Republic and Greek Political Theory, Polis 5.2 (1986), 30-50, and D. Frede, Constitution and Citizenship Peripatetic Influence on Cicero s Political Conceptions in the De re publicaf in Cicero and the Peripatos. 

However, in the case of the electronic orbital model there is no way in which the inter-electronic repulsions can be physically reduced. This form of distinction has not been sufficiently emphasized by philosophers. I believe that the nature of the orbital model shows that not all theoretical models can be lumped together as in the work of Achinstein [1968]. 

The literature of science is replete with models. This variety enables one to make some interesting observations. Thus, for example, one rarely regards models as unique or absolute, although, through the choice of a specific one (e.g., a differential equation), unique solutions to problems may be obtained. A model is formulated to serve a specific purpose. Some models may be suitable for generalization, others may not be. These generalizations are more profitably made as extrapolations for scientific purposes, and occasionally as useful philosophical observations. A model must be flexible to absorb new information, and, hence, stochastic processes have broader and richer applicability than deterministic models. 

Their theory can also be regarded as the begiiming of micro-macro thinking in the written history of science in a philosophical manner, macroscopic properties are projected, but not transferred on a pttre hypothetical microscopic model (Weillbach, 1971). 

Woody, A. (1995). The explanatory power of our models A philosophical analysis with some implications for science education. In F. Finley, D. Allchin, D. Rhees, S. Fifields (Eds.),Proceeding of the third international history, philosophy, and science teaching conference (Vol. 2. pp. 1295-1304). Minneapolis University of Minnesota. 

A good model is consistent with physical phenomena (i.e., 01 has a physically plausible form) and reduces crresidual to experimental error using as few adjustable parameters as possible. There is a philosophical principle known as Occam s razor that is particularly appropriate to statistical data analysis when two theories can explain the data, the simpler theory is preferred. In complex reactions, particularly heterogeneous reactions, several models may fit the data equally well. As seen in Section 5.1 on the various forms of Arrhenius temperature dependence, it is usually impossible to distinguish between mechanisms based on goodness of fit. The choice of the simplest form of Arrhenius behavior (m = 0) is based on Occam s razor. 

A striking feature of the cellular automata (CA) models is that they treat not only the ingredients, or agents, of the model as discrete entities, as do the traditional models of physics and chemistry, but now time (iterations) and space (the cells) are also regarded as discrete, in stark contrast to the continuous forms for these parameters assumed in the traditional, equation-based models. In practice, as we shall see, this distinction makes little or no difference, for the traditional continuous results appear, quite naturally, as limiting cases of the discrete CA analyses. Nonetheless, this quantization of time and space does raise some interesting theoretical and philosophical questions, which we shall, however, ignore at this time. ... 

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