Marcel Weber

Recent discussion of the statistical character of evolutionary theory has centered around two positions: Determinism combined with the claim that the statistical character is eliminable, a subjective interpretation of probability, and instrumentalism; Indeterminism combined with the claim that the statistical character is ineliminable, a propensity interpretation of probability, and realism. I point out some internal problems in these positions and show that the relationship between determinism, eliminability, realism, and the interpretation of probability is more complex than previously assumed in this debate. Furthermore, I take some initial steps towards a more adequate account of the statistical character of evolutionary theory.

Historians of biology have argued that much of the dynamics of experimental disciplines such as genetics or molecular biology can be understood from studying experimental systems and model organisms alone . Such accounts contrast sharply with more traditional philosophies of science which viewed scientific research essentially as a process of inventing and testing theories. I present a case from the history of biochemistry which can be viewed from both the experimental systems perspective and from the methodology of theory testing. I argue that not only are the two perspectives fully compatible, but they are both necessary for a complete account of the research process

I want to exhibit the deeper metaphysical reasons why some common ways of describing the causal role of genes in development and evolution are problematic. Specifically, I show why using the concept of information in an intentional sense in genetics is inappropriate, even given a naturalistic account of intentionality. Furthermore, I argue that descriptions that use notions such as programming, directing or orchestrating are problematic not for empirical reasons, but because they are not strictly causal. They are intentional. By contrast, other notions that are part of the received view in genetics and evolutionary theory are defensible if understood correctly, in particular the idea that genes are the main replicators in evolution. The paper concludes that dropping all intentional or intentionally laden concepts does not force us to accept the so-called causal parity thesis, at least not in its stronger form.

This paper examines how experimental scientists choose theoretical frameworks as well as their experimental systems for doing research. I start out with Kuhn's claim that there are no algorithms that could determine the coices made by individual scientists. Samir Okasha has recently provided an argument for this claim in terms of social choice theory, which I briefly discuss. Then, I show why this problem is not relevant in an experimental science. There are social mechanisms in place that make sure the community chooses the best framework and a matching experimental system. As historical evidence for this claim, I present the case of classical genetics.