In the Origin, Darwin forwards two incompatible lines of attack on special creationism. First, he argues that imperfect or functionless traits are evidence against design. Second, he argues that since special creationism can be made compatible with any observation, it is unscientific and explanatorily vacuous. In later works, Darwin shifts to an argument that he finds much more persuasive and which would undermine theistic evolutionism as well. He argues that variation is random with respect to selection and that this demonstrates (...) that there is no design in the biological world. I examine why Darwin found the argument from independence of variation and selection more compelling than the argument from imperfection. I argue that Darwin utilized general principles of causal inference, akin to those used in modern causal modeling, to rule out any unified cause behind the evolutionary process. (shrink)

The desire to minimize the number of individual new entities postulated is often referred to as quantitative parsimony. Its influence on the default hypotheses formulated by scientists seems undeniable. I argue that there is a wide class of cases for which the preference for quantitatively parsimonious hypotheses is demonstrably rational. The justification, in a nutshell, is that such hypotheses have greater explanatory power than less parsimonious alternatives. My analysis is restricted to a class of cases I shall refer to as (...) additive. Such cases involve the postulation of a collection of qualitatively identical individual objects which collectively explain some particular observed phenomenon. Especially clear examples of this sort occur in particle physics. 1 Introduction 2 Particle physics: a case study 3 Three kinds of simplicity 4 Explanatory power 5 Explanation and non-observation 6 Parsimony and scientific methodology 7 Conclusions. (shrink)

This article argues that a particular notion of rationality, more exactly a specific notion of legitimate inference, is presupposed by much work in the social sciences to their detriment. The author describes the notion of rationality he has in mind, explains why it is misguided, identifies where and how it affects social research, and illustrates why that research is weaker as a result. The notion of legitimate inference the author has in mind is one that believes inferences are guided by (...) principles that are formal, universal and a priori—that is, that make no substantive, domain-specific empirical assumptions about the world. The author briefly provides a variety of reasons to be skeptical about such principles. Those reasons extend from broad philosophical considerations to quasi-empirical evidence. The author then argues that this notion of inference is involved with both the practice and the content of the social sciences in various ways. In terms of practice, this notion of inference lies behind the way statistical testing is generally done in the social sciences. In terms of content, the author argues that this notion of rationality is invoked, for example, in game theory and in rational expectations macroeconomics. In all cases, use of the formal rationality notion leads social scientists to put more faith in their results than is warranted and thus is an obstacle to doing better social science. (shrink)