Elliott Sober

It is a challenge to explain how evolutionary altruism can evolve by the process of natural selection, since altruists in a group will be less fit than the selfish individuals in the same group who receive benefits but do not make donations of their own. Darwin proposed a theory of group selection to solve this puzzle. Very simply, even though altruists are less fit than selfish individuals within any single group, groups of altruists are more fit than groups of selfish individuals. If a population is subdivided into many groups that vary in their altruistic tendencies, altruism will be favored at the level of selection among groups even as it is being disfavored at the level of selection among individuals within groups. Darwin’s scenario became the basis for a theoretical framework called multilevel selection theory.

The positivists suggest that some truths may be immune from empirical refutation and yet lacking in rational justification. Quine holds that every proposition is in principle empirically refutable so there are no a priori truths. I’ll provide a working characterization of the idea of “rational revisability” and argue it’s impossible for us to take a chain of rational revision and end up revising everything which we now believe. Quine's position on revisability is also in tension with certain theses about epistemic holism. Finally, I discuss a more general context in which rational argument is viewed as one mechanism among many for reliably modifying beliefs. This more biological perspective allows us to formulate Quine's claims about revisability in a new way and to raise some interesting questions about the potential impact of natural selection on our cognitive capacities.

Historical sciences like evolutionary biology reconstruct past events by using the traces that the past has bequeathed to the present. Markov chain theory entails that the passage of time reduces the amount of information that the present provides about the past. Here we use a Moran process framework to show that some evolutionary processes destroy information faster than others. Our results connect with Darwin’s principle that adaptive similarities provide scant evidence of common ancestry whereas neutral and deleterious similarities do better. We also describe how the branching in phylogenetic trees affects the information that the present supplies about the past

The logical empiricists said some good things about epistemology and scientific method. However, they associated those epistemological ideas with some rather less good ideas about philosophy of language. There is something epistemologically suspect about statements that cannot be tested. But to say that those statements are meaningless is to go too far. And there is something impossible about trying to figure out which of two empirically equivalent theories is true. But to say that those theories are synonymous is also to go too far. My goal in this paper is not to resuscitate all these positivist ideas, but to revisit just one of them. Instrumentalism is the idea that theories are instruments for making predictions. Of course, no one would disagree that this is one of the things we use theories to do. In just the same way, no one could disagree with the emotivist claim that one of the things we do with ethical terms like "good" and "right" is to express our feelings of approval and disapproval. Instrumentalism and emotivism become contentious, and therefore interesting, when these claims are supplemented.

A phylogeny that allows for lateral gene transfer (LGT) can be thought of as a strictly branching tree (all of whose branches are vertical) to which lateral branches have been added. Given that the goal of phylogenetics is to depict evolutionary history, we should look for the best supported phylogenetic network and not restrict ourselves to considering trees. However, the obvious extensions of popular tree-based methods such as maximum parsimony and maximum likelihood face a serious problem—if we judge networks by fit to data alone, networks that have lateral branches will always fit the data at least as well as any network that restricts itself to vertical branches. This is analogous to the well-studied problem of overfitting data in the curve-fitting problem. Analogous problems often have analogous solutions and we propose to treat network inference as a case of model selection and use the Akaike Information Criterion (AIC). Strictly tree-like networks are more parsimonious than those that postulate lateral as well as vertical branches. This leads to the conclusion that we should not always infer LGT events whenever it would improve our fit-to-data, but should do so only when the improved fit is larger than the penalty for adding extra lateral branches.