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All extant purely probabilistic measures of explanatory power satisfy the following technical condition: if Pr(E | H1) > Pr(E | H2) and Pr(E | ∼H1) < Pr(E | ∼H2), then H1’s explanatory power with respect to E is greater than H2’s explanatory power with respect to E. We argue that any measure satisfying this condition faces three serious problems—the Problem of Temporal Shallowness, the Problem of Negative Causal Interactions, and the Problem of Nonexplanations. We further argue that many such measures face a fourth problem—the Problem of Explanatory Irrelevance.

Darwinowską teorię ewolucji zwykle postrzega się jako zgodną z wymogami naturalizmu metodologicznego, jak jednak można pogodzić to z faktem, że w O powstawaniu gatunków Darwin wielokrotnie mówił o Bogu? Odpowiedź na to pytanie łączę z objaśnieniem znaczenia naturalizmu metodologicznego. Przy okazji zastanawiam się, czy twierdzenia dotyczące istot nadnaturalnych są kiedykolwiek testowalne i czy uprawianie nauki byłoby niemożliwe, gdyby porzucono naturalizm metodologiczny. Inne pytanie dotyczy tego, czy teoria Darwina oraz jej współczesne następczynie są niezgodne z izolowanymi aktami boskiej interwencji, a kolejne brzmi: jeśli liczby rozumiane są na sposób platoński (jako istniejące poza czasem i przestrzenią), to czy tym samym współczesna zmatematyzowana teoria ewolucji sprzeniewierza się naturalizmowi metodologicznemu?

Kiedy rzecznicy teorii inteligentnego projektu (ID) zaprzeczają, że ich teoria ma charakter religijny, opowiadają się za teorią minimalistyczną (teorią mini-ID), według której odkryte w naturze nieredukowalnie złożone adaptacje są dziełem jednego lub większej liczby inteligentnych projektantów. Nie uznają jej za religijną, ponieważ nie określa ona tożsamości projektanta – dzieła stworzenia mógł dokonać nadprzyrodzony Bóg lub grupa istot pozaziemskich. W niniejszym artykule próbuję wykazać, że teza ta bagatelizuje konsekwencje płynące z teorii mini-ID. Teoria mini-ID uzupełniona o kolejne cztery założenia, których wiarygodność została ustalona niezależnie, pociąga istnienie nadprzyrodzonego inteligentnego projektanta. W dalszej części artykułu dowodzę, że takie teorie naukowe, jak darwinowska teoria ewolucji, nie wypowiadają się na temat istnienia nadprzyrodzonych projektantów.

The article is a critical analysis of the explanatory filter concept presented by William Dembski in his book The Design Inference. The filter is understood as formalization of the procedure leading to detection whether a given event is designed. The procedure is based on application of the rules that determine the choice between mutually exclusive hypotheses: Regularity, Chance and Design. Authors refer to the notion of likelihood of a hypothesis understood as the probability that hypothesis confers on observations. They argue that, in contrast to the likelihood of Chance and Regularity hypotheses, Dembski never takes the likelihood of Design hypothesis into consideration. Authors also criticize classification of above mentioned hypotheses proposed in The Design Inference.

The disjunction problem and the distality problem each presents a challenge that any theory of mental content must address. Here we consider their bearing on purely probabilistic causal (ppc) theories. In addition to considering these problems separately, we consider a third challenge—that a theory must solve both. We call this “the hard problem.” We consider 8 basic ppc theories along with 240 hybrids of them, and show that some can handle the disjunction problem and some can handle the distality problem, but none can handle the hard problem. This is our main result. We then discuss three possible responses to that result, and argue that though the first two fail, the third has some promise.

Allen and Pardo’s explanation of Relative Plausibility as a theory of evidence and proof in litigation is ambiguous and underspecified. Their account suggests at least three different interpretations of what they mean. They might be advocating “anti-halfism,” which tracks the “conventional account” but merely rejects >0.5 as the proper standard of proof. Or they might be advocating “probabilistic holism,” in which trial decision-makers apply probability to whole claims but not elements – in which case it remains to be explained how such an approach is internally coherent. Or they might be endorsing “total anti-probabilism,” in which “plausibility” obeys rules and axioms different from those of probability – rules and axioms that Allen and Pardo have yet to identify. To date, Allen and Pardo have side-stepped criticisms by shifting from one interpretation to another, strategically. Aside from presenting a theory too formless to determine how well it fits actual jury behavior, Allen and Pardo have not presented any robust empirical observations about how juries actually decide cases (despite their claims to do so). Before we can really assess whether Relative Plausibility is a new paradigm for understanding the structure of evidence and proof in litigation, Allen and Pardo must tell us much more about what it actually is.

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 (2013, 2014) 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(H | O & E) = Pr(H | O). We defended this screening-off thesis (SOT) by discussing an example concerning smoking and cancer. Climenhaga (Philos Sci, forthcoming) 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”.

> A knowledge of the historic and philosophical background gives that kind of independence from prejudices of his generation from which most scientists are suffering. This independence created by philosophical insight is—in my opinion—the mark of distinction between a mere artisan or specialist and a real seeker after truth. > > Albert Einstein, Letter to Robert Thornton, 1944 Despite the tight historical links between science and philosophy, present-day scientists often perceive philosophy as completely different from, and even antagonistic to, science. We argue here that, to the contrary, philosophy can have an important and productive impact on science. Despite the tight historical links between science and philosophy, hearkening back to Plato, Aristotle, and others (here evoked with Raphael’s famous School of Athens), present-day scientists often perceive philosophy as completely different from, and even antagonistic to, science. To the contrary, we believe philosophy can have an important and productive impact on science. Image credit: Shutterstock.com/Isogood_patrick. We illustrate our point with three examples taken from various fields of the contemporary life sciences. Each bears on cutting-edge scientific research, and each has been explicitly acknowledged by practicing researchers as a useful contribution to science. These and other examples show that philosophy’s contribution can take at least four forms: the clarification of scientific concepts, the critical assessment of scientific assumptions or methods, the formulation of new concepts and theories, and the fostering of dialogue between different sciences, as well as between science and society. ### Conceptual Clarification and Stem Cells. First, philosophy offers conceptual clarification. Conceptual clarifications not only improve the precision and utility of scientific terms but also lead to novel experimental investigations because the choice of a given conceptual framework strongly constrains how experiments are conceived. The definition of stem cells is a prime example. Philosophy has a long tradition of investigating properties, and the tools in use in this tradition … [↵][1]2To whom correspondence should be addressed. Email: thomas.pradeu{at}u-bordeaux.fr. [1]: #xref-corresp-1-1

Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importance. Here we explore its philosophical implications. We outline the nature of Stein’s result and of subsequent work on shrinkage estimators; then we describe how these results are related to Bayesianism and to model selection criteria like AIC. We also discuss their bearing on scientific realism and instrumentalism. We argue that results concerning shrinkage estimators underwrite a surprising form of holistic pragmatism.

We begin by considering two principles, each having the form causal completeness ergo screening-off. The first concerns a common cause of two or more effects; the second describes an interm...

For several decades, evidence theorists have puzzled over the following paradox, known as the "conjunction problem." Probability theory appears to tell us that the probability of a conjunctive claim is the product resulting from multiplying the probabilities of its separate conjuncts. In a three element negligence case (breach of duty, causation, damages), a plaintiff who proves each element to a 0.6 probability, will have proven her overall claim to a very low probability of 0.216. Either the plaintiff wins the verdict based on this low probability (if the jury focuses on elements) or the plaintiff loses despite having met the condition of proving each element to the stated threshold. To solve this "conjunction problem," evidence theorists have advanced such proposals as changing the rules of probability, barring probability theory entirely from analysis of adjudicative factfinding, abandoning the procedural principle that the defendant need not present a narrative of innocence or non-liability, or dispensing with the requirement that the overall claim must meet an established burden of proof. This article argues that the conjunction paradox in fact presents a theoretical problem of little if any consequence. Dropping the condition that proving each element is a sufficient, as opposed to merely a necessary condition for proof of a claim, makes the conjunction problem disappear. Nothing in logic or probability theory requires this "each element/sufficiency" condition, and the legal decision rules reflected in most jury instructions do not mandate it. Once this "each element/sufficiency" condition is removed, all that is left of the conjunction problem is a "probability gap," an intuitive but ill-founded impression that the mathematical underpinnings of the conjunction problem are "unfair" to claimants. This probability gap is considerably narrowed by recognizing the probabilistic dependence of most facts internal to a given claim, and by applying the correct multiplication rule for probabilistically dependent events. Finally, the article argues that solving the conjunction problem is an insufficient ground either to abandon probability theory as a useful analytical tool in the context of adjudicative factfinding, or reform decision rules for trial factfinders.

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.

Markov models of evolution describe changes in the probability distribution of the trait values a population might exhibit. In consequence, they also describe how entropy and conditional entropy values evolve, and how the mutual information that characterizes the relation between an earlier and a later moment in a lineage’s history depends on how much time separates them. These models therefore provide an interesting perspective on questions that usually are considered in the foundations of physics—when and why does entropy increase and at what rates do changes in entropy take place? They also throw light on an important epistemological question: are there limits on what your observations of the present can tell you about the evolutionary past?

I discuss two subjects in Samir Okasha’s excellent book, Evolution and the Levels of Selection. In consonance with Okasha’s critique of the conventionalist view of the units of selection problem, I argue that conventionalists have not attended to what realists mean by group, individual, and genic selection. In connection with Okasha’s discussion of the Price equation and contextual analysis, I discuss whether the existence of these two quantitative frameworks is a challenge to realism.

In their book What Darwin Got Wrong, Jerry Fodor and Massimo Piattelli-Palmarini construct an a priori philosophical argument and an empirical biological argument. The biological argument aims to show that natural selection is much less important in the evolutionary process than many biologists maintain. The a priori argument begins with the claim that there cannot be selection for one but not the other of two traits that are perfectly correlated in a population; it concludes that there cannot be an evolutionary theory of adaptation. This article focuses mainly on the a priori argument.

A phylogeny that allows for lateral gene transfer (LGT) can be thought of as a strictly branching tree (all of whose branches are vertical) to which lateral branches have been added. Given that the goal of phylogenetics is to depict evolutionary history, we should look for the best supported phylogenetic network and not restrict ourselves to considering trees. However, the obvious extensions of popular tree-based methods such as maximum parsimony and maximum likelihood face a serious problem—if we judge networks by fit to data alone, networks that have lateral branches will always fit the data at least as well as any network that restricts itself to vertical branches. This is analogous to the well-studied problem of overfitting data in the curve-fitting problem. Analogous problems often have analogous solutions and we propose to treat network inference as a case of model selection and use the Akaike Information Criterion (AIC). Strictly tree-like networks are more parsimonious than those that postulate lateral as well as vertical branches. This leads to the conclusion that we should not always infer LGT events whenever it would improve our fit-to-data, but should do so only when the improved fit is larger than the penalty for adding extra lateral branches.

This paper is a sympathetic critique of the argument that Reichenbach develops in Chap. 2 of Experience and Prediction for the thesis that sense experience justifies belief in the existence of an external world. After discussing his attack on the positivist theory of meaning, I describe the probability ideas that Reichenbach presents. I argue that Reichenbach begins with an argument grounded in the Law of Likelihood but that he then endorses a different argument that involves prior probabilities. I try to show how this second step in Reichenbach’s approach can be strengthened by using ideas that have been developed recently for understanding causation in terms of the idea of intervention.

After clarifying how Darwin understood natural selection and common ancestry, I consider how the two concepts are related in his theory. I argue that common ancestry has evidential priority. Arguments about natural selection often make use of the assumption of common ancestry, whereas arguments for common ancestry do not require the assumption that natural selection has been at work. In fact, Darwin held that the key evidence for common ancestry comes from characters whose evolution is not caused by natural selection. This raises the question of why Darwin puts natural selection first and foremost in the Origin.

“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 a “classic” phylogenetic inference problem, the observed taxa are assumed to be the leaves of a bifurcating tree and the goal is to infer just the “topology” of the tree (i.e., the formal tree structure linking the extant taxa at the tips), not amount of time between branching events, or amount of evolution that has taken place on branches, or character states of interior vertices. Two of the main methods that biologists now use to solve such problems are maximum likelihood (ML) and maximum parsimony (MP); distance methods constitute a third approach, which will not be discussed here. ML seeks to find the tree topology that confers the highest probability on the observed characteristics of tip species. MP seeks to find the tree topology that requires the fewest changes in character state to produce the characteristics of those tip species. Besides saying what the “best” tree is for a given data set, both methods also provide an ordering of trees, from best to worst. The two methods sometimes disagree about this ordering—most vividly, when they disagree about which tree is best supported by the evidence. For this reason, biologists have had to address this methodological dispute head on, rather than setting it aside as a merely “philosophical” dispute of dubious relevance to scientists “in the trenches.” The main objection that has been made against ML is that it requires the adoption of a model of the evolutionary process that one has scant reason to think is true. ML requires a process model because hypotheses that specify a tree topology (and nothing more) do not, by themselves, confer probabilities on the observations. The situation here is familiar to philosophers as an instance of “Duhem’s Thesis.” Pierre Duhem was a French philosopher of science who contended that physical theories do not entail1 claims about observations unless they are supplemented with auxiliary assumptions (Duhem, 1914). The American philosopher W.V. Quine (1953) later generalized Duhem’s thesis, claiming that all hypotheses fail to entail observational predictions all by themselves. The present point about genealogical hypotheses gives

We explore the evidential relationships that connect two standard claims of modern evolutionary biology. The hypothesis of common ancestry (which says that all organisms now on earth trace back to a single progenitor) and the hypothesis of natural selection (which says that natural selection has been an important influence on the traits exhibited by organisms) are logically independent; however, this leaves open whether testing one requires assumptions about the status of the other. Darwin noted that an extreme version of adaptationism would undercut the possibility of making inferences about common ancestry. Here we develop a converse claim-hypotheses that assert that natural selection has been an important influence on trait values are untestable unless supplemented by suitable background assumptions. The fact of common ancestry and a claim about quantitative genetics together suffice to render such hypotheses testable. Furthermore, we see no plausible alternative to these assumptions; we hypothesize that they are necessary as well as sufficient for adaptive hypotheses to be tested. This point has important implications for biological practice, since biologists standardly assume that adaptive hypotheses predict trait associations among tip species. Another consequence is that adaptive hypotheses cannot be confirmed or disconfirmed by a trait value that is universal within a single species, if that trait value deviates even slightly from the optimum.

I discuss two versions of the doomsday argument. According to ``Gott's Line'',the fact that the human race has existed for 200,000 years licences the predictionthat it will last between 5100 and 7.8 million more years. According to ``Leslie'sWedge'', the fact that I currently exist is evidence that increases the plausibilityof the hypothesis that the human race will come to an end sooner rather than later.Both arguments rest on substantive assumptions about the sampling process thatunderlies our observations. These sampling assumptions have testable consequences,and so the sampling assumptions themselves must be regarded as empirical claims.The result of testing some of these consequences is that both doomsday argumentsare empirically disconfirmed.

Darwin claims in the {\em Origin} that similarity is evidence for common ancestry, but that adaptive similarities are "almost valueless" as evidence. This 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., Comment: 23 pages, 5 figures

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. Since mindless organisms can be evolutionarily altruistic, evolutionary altruism does not entail psychological altruism. And since caring about the welfare of others can, unbeknownst to the actor, enhance the actor's fitness, neither does psychological altruism entail evolutionary altruism. If parents care about their children as ends in themselves, these parents are psychological altruists; but if this caring allows parents to be more reproductively successful, this is not an instance of evolutionary altruism. In Unto Others (hereafter UO), we consider both evolutionary altruism (Part I) and psychological altruism (Part II) from an evolutionary perspective. 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. We think that Darwin was on the right track. Altruistic behaviors can evolve by group selection; the process of individual selection, on the other hand, because it favors individuals who are more fit over other individuals in

Akaike's framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike's framework helps provide instrumentalism with the epistemology it needs. Akaike's criterion for model selection also throws light on the role of parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike's framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered.

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.

Instrumentalism Revisited

Reductionism is often understood to include two theses: (1) every singular occurrence that the special sciences can explain also can be explained by physics; (2) every law in a higher-level science can be explained by physics. These claims are widely supposed to have been refuted by the multiple realizability argument, formulated by Putnam (1967, 1975) and Fodor (1968, 1975). The present paper criticizes the argument and identifies a reductionistic thesis that follows from one of the argument's premises.

Critique de l'ouvrage de W. A. Dembsky intitule «L'inference du dessein» (198) qui examine les contextes non-theologiques de l'argument philosophique du dessein. L'A. rejette la methode epistemologique de Dembsky, fondee sur la hasard et la probabilite, qui ne rend pas compte de la these creationniste ni de la theorie evolutionniste de l'univers.

Physicalism -like other isms -has meant different things to different people. The main physicalistic thesis I will discuss here is the claim that all occurrences supervene on physical occurrences -that the physical events and states of affairs at a time determine everything that happens at that time. This synchronic claim has been discussed most in the context of the mind/body problem, but it is fruitful to consider as well how the supervenience thesis applies to what might be termed the organism/body problem. How are the biological properties of a system at a time related to its physical properties at that time?

An empirical procedure is suggested for testing a model that postulates variables that intervene between observed causes and observed effects against a model that includes no such postulate. The procedure is applied to two experiments in psychology. One involves a conditioning regimen that leads to response generalization; the other concerns the question of whether chimpanzees have a theory of mind.

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.

I am grateful to Philip Kitcher for inviting me to comment on Alexander Rosenberg's (1996) review of Philosophy of Biology (Sober 1993) and to Rosenberg for his kind words about my book at the very beginning and the very end of his review. However, I cannot help feeling that most of the material in Rosenberg's review describes a different book from the one I wrote. Of the four philosophical claims that he ascribes to me, only one of them is asserted or implied in Philosophy of Biology. Rosenberg is right that I have argued that many evolutionary laws turn out, when stated carefully, to be a priori mathematical truths. However, he is mistaken in thinking that I am a “historicist” who holds that “fundamental theory in biology is narrative.” Rosenberg also misconstrues my views on the meaning of probability statements in evolutionary theory.