Elliott Sober

Markov models of evolution describe changes in the probability distribution of the trait values a population might exhibit. In consequence, they also describe how entropy and conditional entropy values evolve, and how the mutual information that characterizes the relation between an earlier and a later moment in a lineage’s history depends on how much time separates them. These models therefore provide an interesting perspective on questions that usually are considered in the foundations of physics—when and why does entropy increase and at what rates do changes in entropy take place? They also throw light on an important epistemological question: are there limits on what your observations of the present can tell you about the evolutionary past?

Carl Hempel1 set the tone for subsequent philosophical work on scientific explanation by resolutely locating the problem he wanted to address outside of epistemology. “Hempel’s problem,” as I will call it, was not to say what counts as evidence that X is the explanation of Y. Rather, the question was what it means for X to explain Y. Hempel’s theory of explanation and its successors don’t tell you what to believe; instead, they tell you which of your beliefs (if any) can be said to explain a given target proposition.

Several philosophers have argued recently that semantic properties do play a causal role. 1 It is our view that none of these arguments are satisfactory. Our aim is to reveal some of the deficiencies of these arguments, and to reassess the question in our own way. In section 1, we shall explain in more detail what is involved in the pretheoretical idea of a causally efficacious property and so provide a fuller sense of the issue. In section 2 we shall discuss Fodor's and Kim's arguments that the semantic properties of mental events are causally efficacious. Their tactic is to articulate sufficient conditions for the causal efficacy of a property, and then apply these conditions to the psychological properties under consideration. We shall show that both formulations are inadequate. However, each account can remedy some of the defects of the other. We shall argue that by putting the two together, and modifying this combined account in certain ways, we can arrive at something that might just pass as a general sufficient condition for causal efficacy. We believe that the question of causal efficacy of content is best approached as a question in the philosophy of science. If one wants to know whether a given species of property is efficacious, the most productive strategy is to find a science that investigates the properties and examine the role they are assigned. In keeping with this attitude we shall restrict our discussion to the notion of content deployed within cognitive science. In section 3 we examine the role of content in cognitive science and discuss whether this role suits content to the conditions on efficacy articulated in section two. Our conclusion will be conditional and hedged: on certain views of cognitive science, content will meet the sufficient condition for efficacy. If other views are correct, the question remains open.

Although the justification for using cladistic parsimony to infer phylogenetic trees has been extensively discussed, much less attention has been paid to the use of cladistic parsimony to reconstruct the character states of the ancestral species postulated by an inferred phylogenetic tree. These two problems differ in terms of both their inputs and their outputs, as shown in the following table. In the former, one begins with data on the character states of extant species and tries to find the best supported phylogenetic tree. In the latter, one begins with the tree that is most firmly supported by characteristics C1, C2,... Cn-1; one then takes some new characteristic Cn and records the character states of Cn that attach to the tree’s interior nodes, which represent common ancestors. In both cases, cladistic parsimony solves the problem by finding the hypothesis that requires the fewest changes in character state that are needed to explain the observations.