Elliott Sober

Philosophy of mind is, and for a long while has been, 99% metaphysics and 1% epistemology. Attention is lavished on the question of the nature of mind, but questions concerning how we know about minds are discussed much less thoroughly. University courses in philosophy of mind routinely devote a lot of time to dualism, logical behaviourism, the mind/brain identity theory, and functionalism. But what gets said about the kinds of evidence that help one determine what mental states, if any, an individual occupies? Well, Skinner's puritanical disdain for postulating mental states gets raked over the coals, the problem of other minds gets solved by a perfunctory invocation of the principle of inference to the best explanation, and the Turing test gets discussed, mainly in order to emphasize that it can lead to mistaken answers.

In what follows I will discuss an example of the Duhem-Quine problem in which Pr(H A), Pr(A H), and Pr(OI +H& ?A) (where H is the hypothesis, A the auxiliary assumptions, and O the observational prediction) can be construed objectively; however, only some of those quantities are relevant to the analysis that I provide. The example involves medical diagnosis. The goal is to test the hypothesis that someone has tuberculosis; the auxiliary assumptions describe the er- ror characteristics of the test procedure. Although it can make sense to talk about the objective probability that someone (randomly drawn from a given population) has tuberculosis and it also can make sense to talk about the objective probability that a test procedure has a certain set of error characteristics, neither of these quantities will enter into the analysis. The analysis proceeds entirely via likelihoods, what one needs to consider is just the probability of the observations conditional on four conjunctions of the form (?H & +A).' It is a special feature of the example that all four of these conjunctions are simple statistical hypotheses in the technical sense that each unambigu-ously confers a probability on the observations.'o After describing how the likelihood concept applies to the example concerning medical diagnosis, I will show how similar patterns can arise in the context of a second inferential framework-that of H. Akaike's criterion of model selection;" this time the example will involve phylogenetic inference.

Is all explanations causal explanation? Puzzles about barometer readings "explain" storms and shadow lengths "explaining" flagpole heights make it attractive to think so. Wesley Salmon (1984) has endorsed this causal thesis. One way to test this thesis is to assess the explanatory import of pseudo-processes. I do so by discussing the concept of heritability, which measures a pseudo-process, and one role it played in the theory of natural selection: explaining response to selection. This will show, not just that heritability has heuristic or predictive utility, but that it can be explanatory. Possible responses to this case are also discussed.

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.

The propensity interpretation of fitness draws on the propensity interpretation of probability, but advocates of the former have not attended sufficiently to problems with the latter. The causal power of C to bring about E is not well-represented by the conditional probability Pr. Since the viability fitness of trait T is the conditional probability Pr, the viability fitness of the trait does not represent the degree to which having the trait causally promotes surviving. The same point holds for fertility fitness. This failure of trait fitness to capture causal role can also be seen in the fact that coextensive traits must have the same fitness values even if one of them promotes survival and the other is neutral or deleterious. Although the fitness of a trait does not represent the trait’s causal power to promote survival and reproduction, variation in fitness in a population causally promotes change in trait frequencies; in this sense, fitness variation is a population-level propensity

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.

This paper defends two theses about probabilistic reasoning. First, although modus ponens has a probabilistic analog, modus tollens does not – the fact that a hypothesis says that an observation is very improbable does not entail that the hypothesis is improbable. Second, the evidence relation is essentially comparative; with respect to hypotheses that confer probabilities on observation statements but do not entail them, an observation O may favor one hypothesis H1 over another hypothesis H2 , but O cannot be said to confirm or disconfirm H1 without such relativization. These points have serious consequences for the Intelligent Design movement. Even if evolutionary theory entailed that various complex adaptations are very improbable, that would neither disconfirm the theory nor support the hypothesis of intelligent design. For either of these conclusions to follow, an additional question must be answered: With respect to the adaptive features that evolutionary theory allegedly says are very improbable, what is their probability of arising if they were produced by intelligent design? This crucial question has not been addressed by the ID movement.

Only one traditional objection to Pascal's wager is telling: Pascal assumes a particular theology, but without justification. We produce two new objections that go deeper. We show that even if Pascal's theology is assumed to be probable, Pascal's argument does not go through. In addition, we describe a wager that Pascal never considered, which leads away from Pascal's conclusion. We then consider the impact of these considerations on other prudential arguments concerning what one should believe, and on the more general question of when and why belief formation ought to be based solely on the evidence.

In both biology and the human sciences, social groups are sometimes treated as adaptive units whose organization cannot be reduced to individual interactions. This group-level view is opposed by a more individualistic one that treats social organization as a byproduct of self-interest. According to biologists, group-level adaptations can evolve only by a process of natural selection at the group level. Most biologists rejected group selection as an important evolutionary force during the 1960s and 1970s but a positive literature began to grow during the 1970s and is rapidly expanding today. We review this recent literature and its implications for human evolutionary biology. We show that the rejection of group selection was based on a misplaced emphasis on genes as "replicators" which is in fact irrelevant to the question of whether groups can be like individuals in their functional organization. The fundamental question is whether social groups and other higher-level entities can be "vehicles" of selection. When this elementary fact is recognized, group selection emerges as an important force in nature and what seem to be competing theories, such as kin selection and reciprocity, reappear as special cases of group selection. The result is a unified theory of natural selection that operates on a nested hierarchy of units. The vehicle-based theory makes it clear that group selection is an important force to consider in human evolution. Humans can facultatively span the full range from self-interested individuals to "organs" of group-level "organisms." Human behavior not only reflects the balance between levels of selection but it can also alter the balance through the construction of social structures that have the effect of reducing fitness differences within groups, concentrating natural selection (and functional organization) at the group level. These social structures and the cognitive abilities that produce them allow group selection to be important even among large groups of unrelated individuals.

Presented in an engaging lecture-style format, this anthology leads readers through a series of discussions on the basic issues and ideas in philosophy, with lectures supported by related readings from historically important sources. The discussions emphasize the logic of philosophical arguments—and in particular, how they relate to the content of scientific theories such as evolution. This five-part book, made up of “lectures” and readings, covers an introduction to philosophy; the philosophy of religion; theory of knowledge; philosophy of mind; and ethics. For anyone looking for adequate answers to enduring questions.

The design argument for the existence of God took a probabilistic turn in the 17 th and 18 th centuries. Earlier versions, such as Thomas Aquinas' 5 th way, usually embraced the premise that goal-directed systems (things that "act for an end" or have a function) must have been created by an intelligent designer. This idea – which we might express by the slogan "no design without a designer" – survived into the 17 th and 18 th centuries, 1 and it is with us still in the writings of many creationists. The new version of the argument, inspired by the emerging mathematical theory of probability, removed the premise of necessity. It begins with the thought that goal-directed systems might have arisen by intelligent design or by chance; the problem is to discern which hypothesis is more plausible. With the epistemic concept of plausibility characterized in terms of the mathematical concept of probability, the design argument was given a new direction. The new probabilistic perspective did not extinguish the older idea of "no design without a designer." The two conflicting approaches coexisted, often with less than perfect clarity about their fundamental difference. At the same time, the details of how the probabilistic perspective ought to be articulated were slow in emerging. These characteristics of uneven development are exemplified in a debate that took place in the 18 th century among three probabilists – John Arbuthnot, Nicholas Bernoulli, and Abraham DeMoivre – on the proper explanation of human sex ratio. Arbuthnot proposed a probabilistic version of the design argument, claiming that it is intelligent design, not chance, that provides the better explanation of why slightly more boys than girls are born each year. Bernoulli rejected Arbuthnot's argument. DeMoivre defended Arbuthnot against Bernoulli's criticisms. The problem of explaining sex ratio, both in human populations and in the rest of nature, experienced another transformation after 1859 --it became a problem for the theory of evolution by natural selection. Darwin (1871) addressed the question in the first edition of The Descent of Man but withdrew his suggestion in the second (Darwin 1874). Carl Düsing (1884) provided a mathematical model that moved beyond the explanation Darwin offered. Fisher (1930) added the new idea of parental expenditure. Williams (1966) argued that the sex ratios found in nature provide the opportunity to test hypotheses of group selection against hypotheses of individual selection. Hamilton (1967) constructed a model that generalizes Fisher's approach and represents the effects of both group and individual selection. The theory of natural selection gradually developed the ability to predict sex ratios, not just explain them post hoc.

Maximum Parsimony (MP) and Maximum Likelihood (ML) are two methods for evaluating which phlogenetic tree is best supported by data on the characteristics of leaf objects (which may be species, populations, or individual organisms). MP has been criticized for assuming that evolution proceeds parsimoniously -- that if a lineage begins in state i and ends in state j, the way it got from i to j is by the smallest number of changes. MP has been criticized for needing to assume some model or other of the evolutionary process even though biologists often do not know what that process really is. This paper critically evaluates both criticisms.

The chapter discusses the principle of conservatism and traces how the general principle is related to the specific one. This tracing suggests that the principle of conservatism needs to be refined. Connecting the principle in cognitive science to more general questions about scientific inference also allows us to revisit the question of realism versus instrumentalism. The framework deployed in model selection theory is very general; it is not specific to the subject matter of science. The chapter outlines some non-Bayesian ideas that have been developed in model selection theory. The principle of conservatism, like C. Lloyd Morgan's canon, describes a preference concerning kinds of parameters. It says that a model that postulates only lower-level intentionality is preferable to one that postulates higher-level intentionality if both fit the data equally well. The model selection approach to parsimony helps explain why unification is a theoretical virtue.

Realism and empiricism have always been contradictory tendencies in the philosophy of science. The view I will sketch is a synthesis, which I call Contrastive Empiricism. Realism and empiricism are incompatible, so a synthesis that merely conjoined them would be a contradiction. Rather, I propose to isolate important elements in each and show that they combine harmoniously. I will leave behind what I regard as confusions and excesses. The result, I hope, will be neither contradiction nor mishmash.

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.