John of Damascus’s Theological Methodology: An Effective Way to Answer Islamic Objections

John of Damascus, who is considered one of the three pillars of the Eastern Orthodox church, was not known in the West for a long time. Few scholars studied his work in recent years and highlighted some aspects of his Summa, which is considered the first systematic theology work in the history of Christianity. This paper will have three sections: the first section shall discuss the life and the educational background of John. The second section shall discuss and evaluate John’s theological methodology. The third section shall discuss his methodology in answering the Saracen. This paper aims to highlight John’s theological work and methodology, and evaluate his answers to the Islamic objections

Inference to the Best Explanation and the Screening-Off Challenge

We argue in Roche and Sober (2013) that explanatoriness is evidentially irrelevant in that Pr(H | O&EXPL) = Pr(H | O), where H is a hypothesis, O is an observation, and EXPL is the proposition that if H and O were true, then H would explain O. This is a “screening-off” thesis. Here we clarify that thesis, reply to criticisms advanced by Lange (2017), consider alternative formulations of Inference to the Best Explanation, discuss a strengthened screening-off thesis, and consider how it bears on the claim that unification is evidentially relevant

Theoretical Virtues in Scientific Practice: An Empirical Study

It is a common view among philosophers of science that theoretical virtues (also known as epistemic or cognitive values), such as simplicity and consistency, play an important role in scientific practice. In this paper, I set out to study the role that theoretical virtues play in scientific practice empirically. I apply the methods of data science, such as text mining and corpus analysis, to study large corpora of scientific texts in order to uncover patterns of usage. These patterns of usage, in turn, might shed some light on the role that theoretical virtues play in scientific practice. Overall, the results of this empirical study suggest that scientists invoke theoretical virtues explicitly, albeit rather infrequently, when they talk about models (less than 30%), theories (less than 20%), and hypotheses (less than 15%) in their published works. To the extent that they are mentioned in scientific publications, the results of this study suggest that accuracy, consistency, and simplicity are the theoretical virtues that scientists invoke more frequently than the other theoretical virtues tested in this study. Interestingly, however, depending on whether they talk about hypotheses, theories, or models, scientists may invoke one of those theoretical virtues more than the others

50 words for snow

Scientists and philosophers routinely talk about phenomena, and the ways in which they relate to explanation, theory and practice in science. However, there are very few definitions of the term, which is often used synonymously with "data'', "model'' and in older literature, "hypothesis''. In this paper I will attempt to clarify how phenomena are recognized, categorized and the role they play in scientific epistemology. I conclude that phenomena are not necessarily theory-based commitments, but that they are what explanations are called to account for, which are not presently explained

On Logical and Scientific Strength

The notion of strength has featured prominently in recent debates about abductivism in the epistemology of logic. Following Williamson and Russell, we distinguish between logical and scientific strength and discuss the limits of the characterizations they employ. We then suggest understanding logical strength in terms of interpretability strength and scientific strength as a special case of logical strength. We present applications of the resulting notions to comparisons between logics in the traditional sense and mathematical theories

Finely tuned models sacrifice explanatory depth

It is commonly argued that an undesirable feature of a theoretical or phenomenological model is that salient observables are sensitive to values of parameters in the model. But in what sense is it undesirable to have such 'fine-tuning' of observables (and hence of the underlying model)? In this paper, we argue that the fine-tuning can be interpreted as a shortcoming of the explanatory capacity of the model: in particular it signals a lack of explanatory depth. In support of this argument, we develop a scheme---for models that arise broadly in the sciences---that quantitatively relates fine-tuning of observables described by these models to a lack of depth of explanations based on these models. A significant aspect of our scheme is that, broadly speaking, the inclusion of larger numbers of parameters in a model will decrease the depth of the corresponding explanation. To illustrate our scheme, we apply it in two different settings in which, within each setting, we compare the depth of two competing explanations. The first setting involves explanations for the Euclidean nature of spatial slices of the universe today: in particular, we compare an explanation provided by the big-bang model of the early 1970s (namely, a cosmological model that traces the evolution of the universe back to a singularity without encountering an inflationary period) with an explanation provided by a general model of cosmic inflation. The second setting has a more phenomenological character, where the goal is to infer from a limited sequence of data points, using maximum entropy techniques, the underlying probability distribution from which these data are drawn. In both of these settings we find that our analysis favors the model that intuitively provides the deeper explanation of the observable(s) of interest. We thus provide an account that unifies two 'theoretical virtues' of models used broadly in the sciences---namely, a lack of fine-tuning and explanatory depth---to show that, indeed, finely tuned models sacrifice explanatory depth