Elliott Sober

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.

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.

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.

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.

In this paper, I'll explore three contexts in which the heuristic of personification yields the wrong answer. They all come from game theoretic discussion of altruism and the Prisoner's Dilemma. Whether it is applied to evolution or to rational deliberation, game theory models situations that involve frequency dependence. In the evolutionary case, how fit a trait is, and whether it is more or less fit than the alternatives, depends on the composition of the population (Maynard Smith 1982). In the case of rational deliberation, which act is best for the agent depends on what other actors are likely to do. As we now will see, frequency dependence can throw a monkey wrench into the convenient relationship between deliberation and evolution posited by the heuristic of personification.

The substance of the commentaries, however, reveals considerable disagreement about how UO conceptualizes the idea of group selection. Dennett describes the issues as “mind-twistingly elusive and slippery” and hints that it is mere hype to say that group selection has been revived. Barrett and Godfrey-Smith discuss the problem of multiple perspectives at length and claim that we are too liberal in our definition of groups. We believe that these criticisms obscure the simplicity of the basic question that group selection theory is designed to answer and the historical continuity of that theory, from Darwin to the present.

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.

“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.

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.

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.

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.

My critical remarks' on Alexander Rosenberg’s article on fitness have elicited a rejoinder from Mary Williams and Rosenberg himself. They charge that my criticisms are a “tissue of misunderstandings” (738); since they misunderstand my own position in fundamental ways, it may help to try to clarify the points that divide us. In the interest of brevity, I will ignore technical issues concerning the internal correctness of Williams’s axiomatization and will focus on questions of broader philosophical interest.

In this paper I want to clarify what biologists are talking about when they talk about the evolution of altruism. I'll begin by saying something about the common sense concept. This familiar idea I'll call 'vernacular altruism.' One point of doing this is to make it devastatingly obvious that the common sense concept is very different from the concept as it's used in evolutionary theory. After that preliminary, I'll describe some features of the evolutionary concept. Then I'll conclude by briefly considering what explanatory relation might obtain between vernacular altruism and evolutionary altruism.

A model of a Markov process is presented in which observing the present state of a system is asymmetrically related to inferring the system's future and inferring its past. A likelihood inference about the system's past state, based on observing its present state, is justified no matter what the parameter values in the model happen to be. In contrast, a probability inference of the system's future state, based on observing its present state, requires further information about the parameter values