Elliott Sober

The probability that the fitter of two alleles will increase in frequency in a population goes up as the product of N (the effective population size) and s (the selection coefficient) increases. Discovering the distribution of values for this product across different alleles in different populations is a very important biological task. However, biologists often use the product Ns to define a different concept; they say that drift “dominates” selection or that drift is “stronger than” selection when Ns is much smaller than some threshold quantity (e.g., ½) and that the reverse is true when Ns is much larger than that threshold. We argue that the question of whether drift dominates selection for a single allele in a single population makes no sense. Selection and drift are causes of evolution, but there is no fact of the matter as to which cause is stronger in the evolution of any given allele

God and numbers provide two challenges to metaphysical naturalism–the former if God exists and is a supernatural being, the latter if numbers exist and mathematical Platonism is true. Evolutionary theory is often described as having a commitment to naturalism, but this is doubly wrong. The theory is neutral on the question of whether God exists and mathematical evolutionary theory entails that numbers exist. The chapter develops the point about theistic neutrality by considering what evolutionary biologists mean when they say that mutations are “unguided.” Evolutionary theory is logically compatible with deism and also with various interventionist theologies. In connection with mathematical evolutionary theory, the chapter discusses and criticizes the indispensability argument for mathematical Platonism developed by Quine and Putnam. The chapter criticizes their epistemological holism by considering ideas in Bayesian confirmation theory.

This Element analyzes the various forms that design arguments for the existence of God can take, but the main focus is on two such arguments. The first concerns the complex adaptive features that organisms have. Creationists who advance this argument contend that evolution by natural selection cannot be the right explanation. The second design argument - the argument from fine-tuning - begins with the fact that life could not exist in our universe if the constants found in the laws of physics had values that differed more than a little from their actual values. Since probability is the main analytical tool used, the book provides a primer on probability theory.

There are two concepts of causes, property causation and token causation. The principle I want to discuss describes an epistemological connection between the two concepts, which I call the Connecting Principle. The rough idea is that if a token event of type Cis followed by a token event of type E, then the support of the hypothesis that the first event token caused the second increases as the strength of the property causal relation of C to E does. I demonstrate the principle, illustrate its application to phylogenies, infections, and rumours, and discuss its consequence for the conceptual distinctness of causal processes from the events they connect. Although I am by no means confident that the Connecting Principle is ultimately correct, it seems to be a useful point of departure into an important aspect of the epistemology of causality.

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of them, the key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments.

Human beings are peculiar. In laboratory experiments, they often cooperate in one-shot prisoners’ dilemmas, they frequently offer 1/2 and reject low offers in the ultimatum game, and they often bid 1/2 in the game of divide-the-cake All these behaviors are puzzling from the point of view of game theory. The first two are irrational, if utility is measured in a certain way.1 The last isn’t positively irrational, but it is no more rational than other possible actions, since there are infinitely many other Nash equilibria besides the one in which both players bid 1/2. At the same time, these behaviors seem to indicate that people are sometimes inclined to be cooperative, fair, and just. In his stimulating new book, Brian Skyrms sets himself the task of showing why these inclinations evolved, or how they might have evolved, under the pressure of natural selection. The goal is not to justify our ethical intuitions, but to explain why we have them.2..

As every philosopher knows, “the design argument” concludes that God exists from premisses that cite the adaptive complexity of organisms or the lawfulness and orderliness of the whole universe. Since 1859, it has formed the intellectual heart of creationist opposition to the Darwinian hypothesis that organisms evolved their adaptive features by the mindless process of natural selection. Although the design argument developed as a defense of theism, the logic of the argument in fact encompasses a larger set of issues. William Paley saw clearly that we sometimes have an excellent reason to postulate the existence of an intelligent designer. If we find a watch on the heath, we reasonably infer that it was produced by an intelligent watchmaker. This design argument makes perfect sense. Why is it any different to claim that the eye was produced by an intelligent designer? Both critics and defenders of the design argument need to understand what the ground rules are for inferring that an intelligent designer is the unseen cause of an observed effect.

Aristotle’s principle that “nature does nothing in vain” (NDNIV) is central to his teleological approach to understanding organisms. First, we argue that James G. Lennox’s influential account of NDNIV is unsuccessful. Second, we propose an alternative account that includes a natural state model. According to a natural state model of development, an organism will develop toward its natural state unless interfering forces prevent that from happening. Third, we argue that this account also fits Aristotle’s discussion in the Generation of Animals and can explain monstrosities in nature that at first sight seem to be counterexamples to NDNIV. Fourth, we take a broader look at NDNIV, arguing that it does not entail that all structures are teleological (since it accommodates the thought that by-products and some neglected items that Aristotle calls “tokens” are nonteleological), that it does not entail that there is only one function per organ, that NDNIV is not an anthropocentric principle, and that, perhaps surprisingly, the teleological structures of an organism can benefit others apart from the organism and its progeny. Finally, we argue that Aristotle endorsed an empirical justification of NDNIV wherein the principle is justified by its many successful applications.

ABSTRACT Darwin claims in the Origin that similarity is evidence for common ancestry, but that adaptive similarities are ‘almost valueless’ as evidence. This second claim seems reasonable for some adaptive similarities but not for others. Here we clarify and evaluate these and related matters by using the law of likelihood as an analytic tool and by considering mathematical models of three evolutionary processes: directional selection, stabilizing selection, and drift. Our results apply both to Darwin’s theory of evolution and to modern evolutionary biology. _1_ Introduction _2_ The Likelihood Framework _3_ A Sufficient Condition for a Similarity to Favour Common Ancestry over Separate Ancestry _4_ The 1/p Criterion and Its Limitations _5_ Directional Selection versus Drift _6_ Stabilizing Selection versus Drift _7_ Going beyond Two Taxa _8_ Conclusions Appendix

According to Michael Friedman’s theory of explanation, a law X explains laws Y1, Y2, …, Yn precisely when X unifies the Y’s, where unification is understood in terms of reducing the number of independently acceptable laws. Philip Kitcher criticized Friedman’s theory but did not analyze the concept of independent acceptability. Here we show that Kitcher’s objection can be met by modifying an element in Friedman’s account. In addition, we argue that there are serious objections to the use that Friedman makes of the concept of independent acceptability.

We argued that explanatoriness is evidentially irrelevant in the following sense: Let H be a hypothesis, O an observation, and E the proposition that H would explain O if H and O were true. Then our claim is that Pr = Pr. We defended this screening-off thesis by discussing an example concerning smoking and cancer. Climenhaga argues that SOT is mistaken because it delivers the wrong verdict about a slightly different smoking-and-cancer case. He also considers a variant of SOT, called “SOT*”, and contends that it too gives the wrong result. We here reply to Climenhaga’s arguments and suggest that SOT provides a criticism of the widely held theory of inference called “inference to the best explanation”.

The hypothesis of group selection fell victim to a seemingly devastating critique in 1960s evolutionary biology. In Unto Others, we argue to the contrary, that group selection is a conceptually coherent and empirically well documented cause of evolution. We suggest, in addition, that it has been especially important in human evolution. In the second part of Unto Others, we consider the issue of psychological egoism and altruism -- do human beings have ultimate motives concerning the well-being of others? We argue that previous psychological and philosophical work on this question has been inconclusive. We propose an evolutionary argument for the claim that human beings have altruistic ultimate motives.

We address the following issues raised by the commentators of our target article and book: the problem of multiple perspectives; how to define group selection; distinguishing between the concepts of altruism and organism; genetic versus cultural group selection; the dark side of group selection; the relationship between psychological and evolutionary altruism; the question of whether the psychological questions can be answered; psychological experiments. We thank the contributors for their commentaries, which provide a diverse agenda for future study of evolution and morality. Our response will follow the organization of our book, distinguishing between evolutionary issues that concern fitness effects and psychological issues that concern motives.

That some propositions are testable, while others are not, was a fundamental idea in the philosophical program known as logical empiricism. That program is now widely thought to be defunct. Quine’s (1953) “Two Dogmas of Empiricism” and Hempel’s (1950) “Problems and Changes in the Empiricist Criterion of Meaning” are among its most notable epitaphs. Yet, as we know from Mark Twain’s comment on an obituary that he once had the pleasure of reading about himself, the report of a death can be an exaggeration. The research program that began in Vienna and Berlin continues, even though many of the specific formulations that came out of those circles are flawed and need to be replaced.

Karl Popper argued in 1974 that evolutionary theory contains no testable laws and is therefore a metaphysical research program. Four years later, he said that he had changed his mind. Here we seek to understand Popper’s initial position and his subsequent retraction. We argue, contrary to Popper’s own assessment, that he did not change his mind at all about the substance of his original claim. We also explore how Popper’s views have ramifications for contemporary discussion of the nature of laws and the structure of evolutionary theory.

The reception so far of Kitcher's Vaulting Ambition reminds me of the old saw about the difference between an optimist and a pessimist. Looking at the same glass of water, the former sees it as half full while the latter sees it as half empty. Some have seen Kitcher's book as a vindication of the possibility of an evolutionary science of human behavior; others have seen it as a devastating critique of the most influential efforts to date to construct such a science. As in the joke about the water glass, both assessments have their point. Sociobiology is a research program, not a unified theory. What one can do is test particular hypotheses within this program and see whether they live up to reasonable standards of rigor and evidence. This is what Kitcher does, with great energy and care.

Suppose that several observations “coincide,” meaning that they are similar in some interesting respect. Is this coinciding a mere coincidence, or does it derive from a common cause? Those who reason about this kind of question—whether they embrace the first answer or the second—often deploy a mode of inference that I call probabilistic modus tollens. In this chapter I criticize probabilistic modus tollens and consider likelihood and Bayesian frameworks for reasoning about coincidences. I also consider the perspective offered by model selection theory (including the Akaike information criterion), and argue that model selection often provides important insights.

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.

Evolutionary theory is awash with probabilities. For example, natural selection is said to occur when there is variation in fitness, and fitness is standardly decomposed into two components, viability and fertility, each of which is understood probabilistically. With respect to viability, a fertilized egg is said to have a certain chance of surviving to reproductive age; with respect to fertility, an adult is said to have an expected number of offspring.1 There is more to evolutionary theory than the theory of natural selection, and here too one finds probabilistic concepts aplenty. When there is no selection, the theory of neutral evolution says that a gene’s chance of eventually reaching fixation is 1/(2N), where N is the number of organisms in the generation of the diploid population to which the gene belongs. The evolutionary consequences of mutation are likewise conceptualized in terms of the probability per unit time a gene has of changing from one state to another. The examples just mentioned are all “forwarddirected” probabilities; they describe the probability of later events, conditional on earlier events. However, evolutionary theory also uses “backwards probabilities” that describe the probability of a cause conditional on its effects; for example, coalescence theory allows one to calculate the expected number of generations in the past that the genes in the present generation find their most recent common ancestor.