Sebastian Lutz

This dissertation consists of three parts. Part I is a defense of an artificial language methodology in philosophy and a historical and systematic defense of the logical empiricists' application of an artificial language methodology to scientific theories. These defenses provide a justification for the presumptions of a host of criteria of empirical significance, which I analyze, compare, and develop in part II. On the basis of this analysis, in part III I use a variety of criteria to evaluate the scientific status of intelligent design, and further discuss confirmation, reduction, and concept formation.

Due to the logical relations between theism and intelligent design (id), there are two challenges to theism that also apply to id. In the falsifiability challenge, it is charged that theism is compatible with every observation statement and thus asserts nothing. I argue that the contentious assumptions of this challenge can be avoided without loss of precision by charging theism (and thus id) directly with the lack of observational assertions. In the translatability challenge, it is charged that theism can be translated into a (non-theistic) set of observation statements without loss of cognitive content. I argue that the contentious assumptions of this challenge are avoided by the related charge that the (non-theistic) evolutionary theory makes all the observational assertions of id, while the converse does not hold. Elliott Sober has argued that id meets the falsifiability challenge, but, since it makes almost no observational assertions, is not testable. I point out two problems with Sober’s argument and show that id is both deductively and probabilistically testable. Sober’s argument, I suggest, inconsistently combines the modified falsifiability challenge with the modified translatability challenge. If his claims about id’s observational assertions are true, however, id succumbs to the modified translatability challenge.

I provide an explicit formulation of empirical adequacy, the central concept of constructive empiricism, and point out a number of problems. Based on one of the inspirations for empirical adequacy, I generalize the notion of a theory to avoid implausible presumptions about the relation of theoretical concepts and observations, and generalize empirical adequacy with the help of approximation sets to allow for lack of knowledge, approximations, and successive gain of knowledge and precision. As a test case, I provide an application of these generalizations to a simple interference phenomenon

I defend the Received View on scientific theories as developed by Carnap, Hempel, and Feigl against a number of criticisms based on misconceptions. First, I dispute the claim that the Received View demands axiomatizations in first order logic, and the further claim that these axiomatizations must include axioms for the mathematics used in the scientific theories. Next, I contend that models are important according to the Received View. Finally, I argue against the claim that the Received View is intended to make the concept of a theory more precise. Rather, it is meant as a generalizable framework for explicating specific theories.

If intelligent design (id) is to compete with evolutionary theory (et), it must meet the modified falsifiability challenge, that is, make some deductive or probabilistic observational assertions. It must also meet the modified translatability challenge, which it fails if et makes all the observational assertions of id, while id does not make all the observational assertions of et. I discuss four prominent but diverse formulations of id and show that each either fails one of the two challenges or is analytically false.

I show how omissions lead to robustness and can justify distortions, and I give inferentially relevant explications of abstraction and idealization. Abstraction is explicated as the omission of all and only those claims that use a specific vocabulary; idealization is explicated as the distortion of only those claims that use a specific vocabulary. With these explications, abstraction can justify idealization. As examples of how abstraction justifies idealization and leads to robustness, I discuss Beauchamp and Childress's four principles of biomedical ethics and the qualitative treatment of the Schrödinger equation.