Here's an overview of one of the more ingenious attempts to criticize religious belief. Antony Flew argues that if the religious won't allow anything to count as evidence against what they believe, then they don't actually believe anything. The religious aren't making false claims; rather, they're not making any claims at all.

Taking a falsificationist perspective, the present paper identifies two major shortcomings of existing approaches to comparative model evaluations in general and strategy classifications in particular. These are (1) failure to consider systematic error and (2) neglect of global model fit. Using adherence measures to evaluate competing models implicitly makes the unrealistic assumption that the error associated with the model predictions is entirely random. By means of simple schematic examples, we show that failure to discriminate between systematic and random error seriously undermines this approach to model evaluation. Second, approaches that treat random versus systematic error appropriately usually rely on relative model fit to infer which model or strategy most likely generated the data. However, the model comparatively yielding the best fit may still be invalid. We demonstrate that taking for granted the vital requirement that a model by itself should adequately describe the data can easily lead to flawed conclusions. Thus, prior to considering the relative discrepancy of competing models, it is necessary to assess their absolute fit and thus, again, attempt falsification. Finally, the scientific value of model fit is discussed from a broader perspective.

According to Karl Popper, we can tell good theories from poor ones by assessing their empirical content (empirischer Gehalt), which basically reflects how much information they convey concerning the world. “The empirical content of a statement increases with its degree of falsifiability: the more a statement forbids, the more it says about the world of experience.” Two criteria to evaluate the empirical content of a theory are their level of universality (Allgemeinheit) and their degree of precision (Bestimmtheit). The former specifies how many situations it can be applied to. The latter refers to the specificity in prediction, that is, how many subclasses of realizations it allows. We conduct an analysis of the empirical content of theories in Judgment and Decision Making (JDM) and identify the challenges in theory formulation for different classes of models. Elaborating on classic Popperian ideas, we suggest some guidelines for publication of theoretical work.

This paper clarifies, revises, and extends the account of the transmission of truthmakers by core proofs that was set out in chap. 9 of Tennant (2017). Brauer provided two kinds of example making clear the need for this. Unlike Brouwer’s counterexamples to excluded middle, the examples of Brauer that we are dealing with here establish the need for appeals to excluded middle when applying, to the problem of truthmaker-transmission, the already classical metalinguistic theory of model-relative evaluations.

This study examines the factors that determine the likelihood of submitting a potentially fraudulent prevented planting claim. A theoretical model is developed and the theoretical predictions are empirically verified by utilizing a binary choice model and crop insurance data from the southern United States. The empirical results show that insured producers with higher prevented planting coverage, lower dollar value of expected yield, and a history of submitting prevented planting claims are more likely to submit an anomalous prevented planting claim. The empirical model also suggests revenue insurance plans may be more vulnerable to prevented planting fraud than the traditional yield-based insurance plan. Results of this study can be valuable to compliance offices in their efforts to find “indicators” of fraudulent behavior in crop insurance, especially with regard to prevented planting.

Scientists observe nature, search for generalizations, and provide explanations for why the world is as it is. Generalizations are of two kinds. The first are descriptive and inductive, such as Boyle's Law. They are derived from observations and therefore refer to observables (in this case, pressure and volume). The second are often imaginative and form the axioms of a deductive theory, such as Newton's Laws of Motion. They often refer to unobservables (e.g. inertia and gravitation). Biology has many inductive generalizations (e.g. Bergmann's Rule and ‘all cells arise from preexisting cells’) but few, if any, recognized universal laws and virtually no deductive theory. Many biologists and philosophers of biology have agreed that predictive theory is inappropriate in biology, which is said to be more complex than physics, and that one can have nonpredictive explanations, such as the neo-Darwinian Theory of Evolution by Natural Selection. Other philosophers dismiss nonpredictive, explanatory theories, including evolutionary ‘theory’, as metaphysics. Most biologists do not think of themselves as philosophers or give much thought to the philosophical basis of their research. Nevertheless, their philosophy shows in the way they do research. The plethora of ad hoc (i.e. not universal) hypotheses indicates that biologists are reluctant inductivists in that the search for generalization does not have a high priority. Biologists test their hypotheses by verification. Theoretical physicists, in contrast, are deductive unifiers and test their explanatory hypotheses by falsification.

I argue that theoretical biology (concerned with unobservables, such as fitness and natural selection) is not scientific because it lacks universal laws and predictive theory. In order to make this argument, I review the differences between verificationism and falsificationism, induction and deduction, and descriptive and explanatory laws. I show how these differ with a specific example of a successful and still useful (even if now superseded as explanatory) deductive theory, Newton's Theory of Motion. I also review some of the philosophical views expressed on these topics because philosophers seem to be even more divided than biologists, which is not at all helpful.

The fact that biology does not have predictive theories does not constitute irrefutable evidence that it cannot have them. The only way to falsify this philosophical hypothesis, however, is to produce a predictive theory with universal biological laws. I have proposed such a theory, but it has been presented piecemeal. At the end of this paper, I bring the pieces together into a deductive theory on the evolution of life history traits (e.g. clutch size, mating relationships, sexual size dimorphism).