Elliott Sober

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 distinction is drawn between property causation and token causation. According to the former, a positive causal factor in a population raises the probability of its effects within "background contexts". The latter, which concerns "actual physical connections" between token events, is not explicated here although its distinctness from the first concept and its importance are discussed. The applicability of both is illustrated by two currently controversial issues in evolutionary theory -- the units of selection controversy and the use of parsimony as an inference rule in phylogenetic reconstruction.

Writers like Attneave [I960], Fodor [I968], and Dennett [1978] have argued that explanations of a mental capacity can only avoid the emptiness of Moliere's dormative virtue by decomposing the capacity into a set of components which are more rudimentary. But What is wrong with smart homunculi? I argue that smart homunculi may explain token events, such as why I now see the page in front of me, but they do not explain what seeing is. It is the importance of the latter explanatory problem which explains why homunculi must be so stupid.

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.

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.

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.

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.

John Beatty (1995) and Alexander Rosenberg (1994) have argued against the claim that there are laws in biology. Beatty's main reason is that evolution is a process full of contingency, but he also takes the existence of relative significance controversies in biology and the popularity of pluralistic approaches to a variety of evolutionary questions to be evidence for biology's lawlessness. Rosenberg's main argument appeals to the idea that biological properties supervene on large numbers of physical properties, but he also develops case studies of biological controversies to defend his thesis that biology is best understood as an instrumental discipline. The present paper assesses their arguments.

In ‘Two Dogmas of Empiricism’, Quine attacks the analytic/synthetic distinction and defends a doctrine that I call epistemological holism. Now, almost fifty years after the article’s appearance, what are we to make of these ideas? I suggest that the philosophical naturalism that Quine did so much to promote should lead us to reject Quine’s brief against the analytic/synthetic distinction; I also argue that Quine misunderstood Carnap's views on analyticity. As for epistemological holism, I claim that this thesis does not follow from the logical point that Duhem and Quine made about the role of auxiliary assumptions in hypothesis testing, and that the thesis should be rejected.

Altruism has both an evolutionary and a psychological meaning. As the term is used in evolutionary theory, a trait is deemed altruistic if it reduces the fitness of the actor and enhances the fitness of someone else. In its psychological sense, the thesis that we have altruistic ultimate motives asserts that we care about the welfare of others, not just as a means of enhancing our own well-being, but as an end in itself. In Unto Others (hereafter UO), we consider both evolutionary altruism (Part I) and psychological altruism (Part II) from an evolutionary perspective. We propose a multilevel selection theory to explain how altruism can evolve from group selection, based on Darwin’s solution to the problem. Our presentation of multilevel selection theory expands the range of topics beyond altruism in other respects as well. In Part I1 of UO, we evaluate the arguments, both conceptual and empirical, that philosophers and psychologists have advanced in an attempt to choose between psychological egoism and motivational pluralism. We conclude that these arguments are inconclusive.

Human nature is intriguing in such that it can express both negative and positive emotions, as in kindness and cruelty. The question is whether both are a natural part of our nature as human beings, or is one produced to serve as the alternate of the other. Another question that is brought to the table in this chapter is how does one determine what is natural and what is not, being its true definition? The chapter attempts to answer these questions based on evolution theory, whether events earlier in history served as a promoter for the emotions of today. Was the nature of kindness and caring inherent in a human being at the beginning of time, or is it only through the changes and events over time that it has developed? The chapter aims to aid the reader in understanding our human tendencies.

How could the fundamental mental operations which facilitate scientific theorizing be the product of natural selection, since it appears that such theoretical methods were neither used nor useful "in the cave"-i.e., in the sequence of environments in which selection took place? And if these wired-in information processing techniques were not selected for, how can we view rationality as an adaptation? It will be the purpose of this paper to address such questions as these, and in the process to sketch some of the considerations that an evolutionary account of rationality may involve. By describing the broad framework within which the evolution of rationality may eventually be understood, I hope to undermine the idea that evolutionary theory is somehow incapable of dealing with this characteristic and requires supplementation by some novel principle. A more modest ambition of the paper is to try to provoke those who think that there are special problems involved in this evolutionary inquiry to say what these problems are.

“Absence of evidence isn’t evidence of absence” is a slogan that is popular among scientists and nonscientists alike. This article assesses its truth by using a probabilistic tool, the Law of Likelihood. Qualitative questions (“Is E evidence about H ?”) and quantitative questions (“How much evidence does E provide about H ?”) are both considered. The article discusses the example of fossil intermediates. If finding a fossil that is phenotypically intermediate between two extant species provides evidence that those species have a common ancestor, does failing to find such a fossil constitute evidence that there was no common ancestor? Or should the failure merely be chalked up to the imperfection of the fossil record? The transitivity of the evidence relation in simple causal chains provides a broader context, which leads to discussion of the fine-tuning argument, the anthropic principle, and observation selection effects.

Evidence and Evolution has four chapters: (1) Evidence, (2) Intelligent Design, (3) Natural Selection, and (4) Common Ancestry. The first chapter develops tools that are used in the rest of the book, though more ideas about evidence are added. In Chapter 1, I endorse a pluralistic outlook—Bayesianism is fine in some inference problems, likelihoodism in others, and AIC in still others. In Chapter Two, on intelligent design, I try to develop the strongest possible formulation of the design argument for the existence of God, after which I criticize the argument so conceived. This second chapter also attempts to resuscitate the concept of testability. Chapter 3, on natural selection, discusses how to evaluate competing hypotheses on evolution mechanisms and evaluate the use of the principle of common cause, molecular data, and cladistic parsimony. Chapter 4 is on common ancestry: what it means to say that all life on earth has a common ancestor, and s. Why should the similarity of two species be evidence that they have a common ancestor? The chapter concludes with a discussion of how biologists use cladistic parsimony to infer phylogenetic trees and compare it with explicit statistical approaches.