Elliott Sober

Psychological egoism is a theory about motivation. It claims that all of our ultimate desires are self‐directed. Whenever we want others to do well (or ill), we have these other‐directed desires only instrumentally; we care about others only because we think that the welfare of others will have ramifications for our own welfare. As stated, egoism is a descriptive, not a normative, claim. It aims to characterize what motivates human beings in fact; the theory does not say whether it is good or bad that people are so motivated.

Suppose 50-year-old Sue now has lung cancer, due to the fact that C = c & A = g (meaning that Sue smoked c cigarettes and inhaled g grams of asbestos over the previous 30 years), and that neither cause caused the other. Given this, a retrospective question arises – did one of those actual causes have a stronger influence than the other on her getting lung cancer? We propose a “zeroing-out” criterion for making sense of this question; it says that C = c was a stronger causal influence than A = g precisely when Pr(lung cancer | C = c & A = g) – Pr(lung cancer | C = 0 & A = g) > Pr(lung cancer | C = c & A = g) – Pr(lung cancer | C = c & A = 0). Zeroing-out uses Pr(lung cancer | C = c & A = g) as a baseline and relates that baseline to two counterfactual probabilities. We discuss how zeroing-out applies to three evolutionary examples − the influences of selection and drift on the fixation of an allele, the influences of group and individual selection on the evolution of altruism, and the influences of stabilizing selection and ancestral influence (aka “phylogenetic inertia”) on the evolution of tetrapody in land vertebrates. Zeroing-out differs from an “adding-in” criterion, which uses Sue’s probability of having lung cancer at age 50, given her actual state at age 20 (at which time she was cancer free and C = 0 & A = 0) as a baseline and asks whether her risk of having lung cancer at age 50 would be greater if C = c were true of the 30 years in between than it would be if A = g were true of those years. Zeroing-out and adding-in generate identical criteria for comparing causal influences in this example because the probabilities are related “monotonically” (a concept we define). We then describe examples in which monotonicity fails and the two criteria differ. We prove theorems that describe when the two criteria disagree and when they do not. We then consider how zeroing-out and adding-in are related to six quantitative measures of causal strength that have been proposed. Inter alia, we discuss how our framework is related to interventionism.

When two causally independent processes each have a quantity that increases monotonically (either deterministically or in probabilistic expectation), the two quantities will be correlated, thus providing a counterexample to Reichenbach's principle of the common cause. Several philosophers have denied this, but I argue that their efforts to save the principle are unsuccessful. Still, one salvage attempt does suggest a weaker principle that avoids the initial counterexample. However, even this weakened principle is mistaken, as can be seen by exploring the concepts of homology and homoplasy used in evolutionary biology. I argue that the kernel of truth in the principle of the common cause is to be found by separating metaphysical and epistemological issues; as far as the epistemology is concerned, the Likelihood Principle is central.

This chapter examines values and scientific evidence. It argues that one way to put the ideal of value freedom is believing that a proposition has good or bad ethical consequences is not evidence for its truth. The evidence relation is a three-place one between data, hypothesis, and background knowledge, thus allowing background knowledge to link fact and value. It is also argued that there is a qualified sense in which the value-free ideal is right: judgments about the moral consequences of a proposition cannot provide new information about its truth.

Abstract: C. Lloyd Morgan’s Canon says that a higher mental faculty should not be postulated to explain an organism’s behavior if the behavior can be explained by the hypothesis that it has a lower faculty. This epistemological principle strongly influenced and continues to influence cognitive science. The focus of this paper is on Morgan’s own argument for the Canon, which philosophers have generally held to be fatally flawed. We disagree. Morgan’s good argument for his canon is grounded in Morgan’s assumption that an organism that has a higher mental faculty must also have a lower one, together with his belief that a new mental faculty evolves only if it provides a new adaptive advantage. Our main goal here is not to defend Morgan’s canon or his argument for it, but to identify the argument’s logical location in Morgan’s larger framework.

_The Nature of Selection_ is a straightforward, self-contained introduction to philosophical and biological problems in evolutionary theory. It presents a powerful analysis of the evolutionary concepts of natural selection, fitness, and adaptation and clarifies controversial issues concerning altruism, group selection, and the idea that organisms are survival machines built for the good of the genes that inhabit them. "Sober's is the answering philosophical voice, the voice of a first-rate philosopher and a knowledgeable student of contemporary evolutionary theory. His book merits broad attention among both communities. It should also inspire others to continue the conversation."-Philip Kitcher, _Nature_ "Elliott Sober has made extraordinarily important contributions to our understanding of biological problems in evolutionary biology and causality. _The Nature of Selection_ is a major contribution to understanding epistemological problems in evolutionary theory. I predict that it will have a long lasting place in the literature."-Richard C. Lewontin.

A proposition is _derivationally robust_ precisely when it is a prediction of each model in an ensemble of models. We show that a recent and influential Bayesian defense of the epistemic merits of derivational robustness analysis faces substantial obstacles. The main reason for this is that a Bayesian characterization of derivational robustness requires one to condition on a logical or mathematical truth. Standardly, however, conditioning on a logical or mathematical truth cannot raise or lower the probability of any proposition, which means confirmation in such cases is impossible. What is required for a Bayesian defense of derivational robustness to be plausible is the adoption of a non-standard probability calculus, but the details of such an account have yet to be worked out in sufficient detail. We close by arguing against the view that agreement among multiple models has a confirmatory status that is the same or similar to agreement among multiple measurements of some quantity. It is straightforward to show that agreement among measurements has epistemic significance. This is not true for derivational robustness.

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.

In Chapter 12 of Warrant and Proper Function, Alvin Plantinga constructs two arguments against evolutionary naturalism, which he construes as a conjunction E&N. The hypothesis E says that “human cognitive faculties arose by way of the mechanisms to which contemporary evolutionary thought directs our attention” (p. 220). With respect to proposition N, Plantinga (p. 270) says “it isn't easy to say precisely what naturalism is,” but then adds that “crucial to metaphysical naturalism, of course, is the view that there is no such person as the God of traditional theism.” Plantinga tries to cast doubt on the conjunction E&N in two ways. His “preliminary argument” aims to show that the conjunction is probably false, given the fact (R) that our psychological mechanisms for forming beliefs about the world are generally reliable. His “main argument” aims to show that the conjunction E&N is self‐defeating – if you believe E&N, then you should stop believing that conjunction. Plantinga further develops the main argument in his unpublished paper “Naturalism Defeated” (Plantinga 1994). We will try to show that both arguments contain serious errors.

What thesis is Hume trying to establish in his essay “On Miracles” and does he succeed? John Earman’s answer to the latter question is clearly conveyed by the title of his new book. Earman uses a Bayesian representation of the problem to make his case. For Earman, this mode of analysis is both perspicuous and nonanachronistic, in that probability reasoning was central to the 18th century debate about miracles in particular and testimony in general. Indeed, one of Hume’s most interesting antagonists, Richard Price, was the person to whom Thomas Bayes entrusted his now-famous essay for posthumous publication. For Earman, Price is the proper Bayesian, while Hume’s essay provides only “rhetoric and schein geld”. Earman’s tone is consistently prosecutorial and sometimes snide; he says that his animus is not so much against Hume himself as against those who smugly invoke Hume’s essay as definitively settling the matter. This tone should not deter potential readers who are convinced that Hume’s essay contains something of value. Earman’s book is interesting and provocative in multiple ways—it places Hume’s essay in its historical setting, it offers an insightful close reading of the text, and it shows how the resources of Bayesianism can be powerfully put to work. Besides Earman’s own essay, the volume also contains Hume’s essay and relevant work by others, including Locke, Spinoza, Samuel Clarke, Price, Laplace, and Babbage. The book would be an excellent choice for an advanced undergraduate or graduate seminar.

Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so.

The units of selection problem, as it is discussed within evolutionary theory, recapitulates some important elements in the dispute between methodological holism and methodological individualism. Holism and individualism have for a long time occupied favored positions in the stable of old warhorses owned and operated by philosophers of social science. These particular old warhorses are thought by many to be in retirement, although there is less than universal agreement about whether holism or individualism won the battle. Part of the point I will make about group versus individual selection is that biologists, would do wellnotto emulate certain aspects of the holism/individualism controversy.

In Adaptation and Natural Selection, George C. Williams linked the distinction between group and individual adaptation with the distinction between group and individual selection. Williams' Principle, as we will call it, says that adaptation at a level requires selection at that level. This is a necessary but not a sufficient condition; for example, group adaptation requires group selection, but the fact that group selection influences a trait's evolution does not suffice for the resulting trait frequency to be a group adaptation. What more is required? In this paper, we describe an answer to this question that has been developed in multilevel selection theory. We also discuss an alternative framework for defining units of adaptation that violates Williams' Principle.

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.

This book explores important topics in Darwin’s theory of evolution and in the twentieth- and twenty-first-century theories that grew out of it. These topics include fitness, adaptation, altruism, intragenomic conflict, units of selection, genetic drift, the randomness of mutation, gradualism, common ancestry, taxonomy, race, phylogenetic inference, and optimality models. The book brings these and other biological topics into contact with numerous philosophical ideas − operationalism, reductionism, conventionalism, null hypotheses and default reasoning, instrumentalism versus realism, likelihoods versus probabilities, model selection, essentialism, falsifiability, parsimony, the principle of the common cause, comparisons of causal power, interpretations of probaiblity, indeterminism, sensitivity to initial conditions, and the knowability of the past.

Jun Otsuka’s excellent book, Thinking about Statistics - the Philosophical Foundations (Otsuka 2023) is mostly organized around the idea that different statistical approaches can be illuminated by linking them to different ideas in general epistemology. Otsuka connects Bayesianism to internalism and foundationalism, frequentism to reliabilism, and the Akaike Information Criterion in model selection theory to instrumentalism. This useful mapping doesn’t cover all the interesting ideas he presents. His discussions of causal inference and machine learning are philosophically insightful, as is his idea that statisticians embrace an assumption that is similar to Hume’s Principle of the Uniformity of Nature. I discuss these topics in what follows, sometimes disagreeing with details while at other times adding ideas that complement those presented in the book.