Recently, Luk mentioned that scientific knowledge both explains and predicts. Do these two functions of scientific knowledge have equal significance, or is one of the two functions more important than the other? This commentary explains why prediction may be mandatory but explanation may be only desirable and optional.

Philosophers have recently expressed interest in accounting for the usefulness of mathematics to science. However, it is certainly not a new concern. Putnam and Quine have each worked out an argument for the existence of mathematical objects from the indispensability of mathematics to science. Were Quine or Putnam to disregard the applicability of mathematics to science, he would not have had as strong a case for platonism. But I think there must be ways of parsing mathematical sentences which account for (...) applicability of mathematics and also do not require us to believe in entities we have no evidence for, other than through reading these sentences literally. We will explore a particular way to interpret sentences of arithmetic which promises to account for their applicability without bringing in metaphysics not also brought in by science. The investigation will be limited to the arithmetic of cardinal numbers. The general strategy is to argue for the analogy between arithmetic and science, rather than to argue for one case having a particular characteristic independently of the other. (shrink)

Moral philosophers are, among other things, in the business of constructing moral theories. And moral theories are, among other things, supposed to explain moral phenomena. Consequently, one’s views about the nature of moral explanation will influence the kinds of moral theories one is willing to countenance. Many moral philosophers are (explicitly or implicitly) committed to a deductive model of explanation. As I see it, this commitment lies at the heart of the current debate between moral particularists and moral generalists. In (...) this paper I argue that we have good reasons to give up this commitment. In fact, I show that an examination of the literature on scientific explanation reveals that we are used to, and comfortable with, non-deductive explanations in almost all areas of inquiry. As a result, I argue that we have reason to believe that moral explanations need not be grounded in exceptionless moral principles. (shrink)

Wesley Salmon has advanced a new model of explanations of particular facts which requires that the explanans contain laws. The laws used in explanations (according to this model) are of the form P(A· C1,B)=p1... P(A· Cn,B)=pn. A condition imposed by Salmon on these laws is that the reference classes, i.e. A· C1... A· Cn, be homogenous with reference to the property B. A reference class A is homogenous with reference to a property B if every property which determines a place (...) selection with reference to B within A is statistically irrelevant to B in A. It is argued here that the concept of homogeneity cannot in general be satisfied in scientific explanations and that even a weaker requirement, "epistemic homogeneity," may be too strong. (shrink)

The idea that good explanations come with strong changes in probabilities has been very common. This criterion is called probabilistic difference-making. Since it is an intuitive criterion and has a long tradition in the literature on scientific explanation, it comes as a surprise that probabilistic difference-making is rarely discussed in the context of interventionist causal explanation. Specificity, proportionality, and stability are usually employed to measure explanatory power instead. This paper is a first step into the larger project of connecting difference-making (...) to the interventionist debate, and it starts by investigating whether probabilistic difference-making is contained in the notion of specificity. The choice of specificity is motivated by the observation that both probabilistic difference-making and specificity build on similar underlying intuitions. When comparing measures for both specificity and probabilistic difference-making, it turns out that the measures are not strictly correlated, and so the thesis that probabilistic difference-making is encoded within specificity has to be rejected. Some consequences of this result are discussed as well. (shrink)

Michael Strevens’ Depth: An Account of Scientific Explanation is an impressive recent contribution to the philosophical literature on explanation. While clearly influenced by several of the leading theories of the later twentieth century, Strevens’ account of explanation is firmly rooted in the causal tradition. His most notable intellectual debts in this regard owe to David Lewis, Wesley Salmon and James Woodward. Still, Strevens sees the work of these theorists as flawed in important respects, and his “kairetic account” of explanation is (...) meant to provide answers to problems his predecessors left unresolved (or poorly resolved, as the case may be). Before examining Strevens’ account in detail we should identify the more significant of these problems and briefly survey the contexts in which they arose. (shrink)

Most defenders of the new mechanistic approach accept ontic constraints for successful scientific explanation (Illari 2013; Craver 2014). The minimal claim is that scientific explanations have objective truthmakers, namely mechanisms that exist in the physical world independently of any observer and that cause or constitute the phenomena-to- be-explained. How can this idea be applied to type-level explanations? Many authors at least implicitly assume that in order for mechanisms to be the truthmakers of type-level explanation they need to be regular (Andersen (...) 2012; Sheredos 2015). One problem of this assumption is that most mechanisms are (highly) stochastic in the sense that they “fail more often than they succeed” (Bogen 2005; Andersen 2012). How can a mechanism type whose instances are more likely not to produce an instance of a particular phenomenon type be the truthmaker of the explanation of that particular phenomenon type? In this paper, I will give an answer to this question. I will analyze the notion of regularity and I will discuss Andersen's suggestion for how to cope with stochastic mechanisms. I will argue that her suggestion cannot account for all kinds of stochastic mechanisms and does not provide an answer as to why regularity grounds type-level explanation. According to my analysis, a mechanistic type- level explanation is true if and only if at least one of the following two conditions is satisfied: the mechanism brings about the phenomenon more often than any other phenomenon (comparative regularity) or the phenomenon is more often brought about by the mechanism than by any other mechanism/causal sequence (comparative reverse regularity). (shrink)

I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...) Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers. (shrink)

The proper function of the heart is pumping the blood. According to what we call the type etiological view, this is because previous tokens of the type HEART were selected for pumping the blood. Nanay :412–431, 2010) argues that the type etiological view is viciously circular. He claims that the only plausible accounts of trait type individuation use proper functions, such that whenever the type etiological view is supplemented with a plausible account of trait type individuation, the result is a (...) view that uses proper functions to explain proper functions. We refine this objection, and argue that Nanay at most establishes a potentially benign definitional circularity. However, we show that the type etiological view’s reliance on types nevertheless generates a vicious regress. Hence the type etiological view is false. We reject dispositional and modal alternatives to the type etiological view for the reason that they either cannot accommodate malfunction, or do so at the cost of proliferation; and then formulate a novel token etiological view that overcomes both problems because it makes no reference to trait types. (shrink)

Different why-questions emerge under different contexts and require different information in order to be addressed. Hence a relevance relation can hardly be invariant across contexts. However, what is indeed common under any possible context is that all explananda require scientific information in order to be explained. So no scientific information is in principle explanatorily irrelevant, it only becomes so under certain contexts. In view of this, scientific thought experiments can offer explanations, should we analyze their representational strategies. Their representations involve (...) empirical as well as hypothetical statements. I call this the “representational mingling” which bears scientific information that can explain events. Buchanan’s thought experiment from constitutional economics is examined to show how mingled representations explain. (shrink)

The Generality Problem is widely recognized to be a serious problem for reliabilist theories of justification. James R. Beebe's Statistical Solution is one of only a handful of attempted solutions that has garnered serious attention in the literature. In their recent response to Beebe, Julien Dutant and Erik J. Olsson successfully refute Beebe's Statistical Solution. This paper presents a New Statistical Solution that countenances Dutant and Olsson's objections, dodges the serious problems that trouble rival solutions, and retains the theoretical virtues (...) that made Beebe's solution so attractive in the first place. There indeed exists a principled, rigorous, conceptually sparse, and plausible solution to the Generality Problem: it is the New Statistical Solution. (shrink)

The aim of this paper is to develop an adequate version of the D-N theory of explanation for particular events and to show how the resulting D-N model can be used as a tool in articulating a regularity theory of causation and an analysis of the truth conditions for counterfactual conditionals. Starting with a basic model that is largely the product of other workers in this field, two new restrictions are formulated in order to construct a version of D-N explanation (...) that does not yield the counterintuitive results that have plagued all earlier versions. An additional condition is then developed that is indispensable for utilizing the D-N model as a tool in formulating a regularity theory of causation. Finally, it is shown how a suitable model of potential D-N explanation facilitates the formulation of a theory of counterfactual conditionals. (shrink)

Peter van Inwagen has given a probabilistic answer to the fundamental question ‘why is there something rather than nothing?’: There is something, because the probability of there being nothing is 0. Some philosophers have recently examined van Inwagen's argument and concluded that it does not really work. Three points are central in their criticism: the premise which states that there is only one empty possible world is false, the premise which states that all possible worlds have the same probability is (...) not plausible and the argument is not significant for the question it sets out to answer. In this paper, I shall show that even if there are many empty worlds, this does not necessarily invalidate the argument in its general lines, the examples they offer to support the intuition that possible worlds may have different probabilities fail, and even if the conclusion of the argument does not really answer the question van Inwagen sets out to answer, it is still not an insignificant response to the question. (shrink)

Hempel seems to hold the following three views: (H1) Understanding is pragmatic/relativistic: Whether one understands why X happened in terms of Explanation E depends on one's beliefs and cognitive abilities; (H2) Whether a scientific explanation is good, just like whether a mathematical proof is good, is a nonpragmatic and objective issue independent of the beliefs or cognitive abilities of individuals; (H3) The goal of scientific explanation is understanding: A good scientific explanation is the one that provides understanding. Apparently, H1, H2, (...) and H3 cannot be all true. Some philosophers think that Hempel is inconsistent, while some others claim that Hempel does not actually hold H3. I argue that Hempel does hold H3 and that he can consistently hold all of H1, H2, and H3 if he endorses what I call the “understanding argument.” I also show how attributing the understanding argument to Hempel can make more sense of his D-N model and his philosophical analysis of the pragmatic aspects of scientific explanation. (shrink)

My title is intended to recall Terence Fine's excellent survey, Theories of Probability [1973]. I shall consider some developments that have occurred in the intervening years, and try to place some of the theories he discussed in what is now a slightly longer perspective. Completeness is not something one can reasonably hope to achieve in a journal article, and any selection is bound to reflect a view of what is salient. In a subject as prone to dispute as this, there (...) will inevitably be many who will disagree with any author's views, and I take the opportunity to apologize in advance to all such people for what they will see as the narrowness and distortion of mine. (shrink)

A subarea of the debate over the nature of evolutionary theory addresses what the nature of the explanations yielded by evolutionary theory are. The statisticalist line is that the general principles of evolutionary theory are not only amenable to a mathematical interpretation but that they need not invoke causes to furnish explanations. Causalists object that construction of these general principles involves crucial causal assumptions. A recent view claims that some biological explanations are statistically autonomous explanations (SAEs) whereby phenomena are accounted (...) for statistically and which prescind from micro-causal details. I raise three major problems for this account and then advance a view which unifies SAEs as mathematical explanations: the MSAE view. The MSAE view not only resolves the issues bedeviling the original SAE account but serves to importantly broaden the class of non-causal explanations in population biology. (shrink)

Introduction: One of the most influential theories of scientific explanation to have emerged in the past two decades is Salmon's causal/mechanical theory (Salmon 1984). According to this account, scientific explanations describe a network of causal processes and interactions. In this paper, I will use an example from evolutionary biology to argue that the causal nexus, as characterized by Salmon, is not rich enough to account for many causal explanations in the sciences.

It it tempting to think that if an outcome had some probability of not occurring, then we cannot explain why that outcome in fact occurred. Despite this intuition, most philosophers of science have come to admit the possibility of indeterministic explanation. Yet some of them continue to hold that if an outcome was not determined, it cannot be explained why that outcome rather than some other occurred. I argue that this is an untenable compromise: if indeterministic explanation is possible, then (...) indeterministic contrastive explanation is possible too. In order to defend this conclusion, I develop an account of contrastive explanation. (shrink)

Concerning any object of philosophical analysis, we can ask several questions, including the two posed in the title of this paper. Despite difficulties in formulating a precise criterion to distinguish causal processes from pseudoprocesses, and causal interactions from mere spatiotemporal intersections, I argue that Salmon answered the first of these questions with extraordinary clarity. The second question, by contrast, has received very little attention. I will present two problems: in the first, it seems that Salmon has provided exactly the conceptual (...) resources needed to solve the problem; in the second, it is difficult to see how causal processes and interactions may be used to shed any light. In general, the way to carry Salmon's program forward will be to demonstrate that these resources can be made to do real philosophical work. (shrink)

Philosophers have used the probabilistic relation of ’screening-off‘ to explicate concepts in the theories of causation and explanation. Brandon has used screening-off relations in an attempt to reconstruct an argument of Mayr and Gould that natural selection acts at the level of the organism. I argue that Brandon‘s reconstruction is unsuccessful.

I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, (...) I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9. (shrink)

This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic (...) difference. On Greeno's view, the essence of explanation lies in the relevance relations expressed by the conditional probabilities that relate the explanans variables to the explanandum variables; on my view, in contrast, the essence of explanation lies in theories viewed as hypothetical structures which deductively entail conditional probability distributions linking the explanans variables and the explanandum variables. The explanatory power of a stochastic theory is identified with information (regarding the values of explanandum variables) which is "absorbed from" the explanans variables. While other information which is "absorbed from" the explanandum variables (through the process of parameter estimation, for example) reflects descriptive power of the theory. I prove that Greeno's measure of transmitted information is a limiting special case of the E-I model, but that the former, unlike the latter, makes no distinction between explanatory power and descriptive power. (shrink)

Philosophers have postulated the existence of God to explain (I) why any contingent objects exist at all rather than nothing contingent, and (II) why the fundamental laws of nature and basic facts of the world are exactly what they are. Therefore, we ask: (a) Does (I) pose a well-conceived question which calls for an answer? and (b) Can God's presumed will (or intention) provide a cogent explanation of the basic laws and facts of the world, as claimed by (II)? We (...) shall address both (a) and (b). To the extent that they yield an unfavourable verdict, the afore-stated reasons for postulating the existence of God are undermined. As for question (I), in 1714, G. W. Leibniz posed the Primordial Existential Question (hereafter 'PEQ'): 'Why is there something contingent at all, rather than just nothing contingent?' This question has two major presuppositions: (1) A state of affairs in which nothing contingent exists is indeed genuinely possible ('the Null Possibility'), the notion of nothingness being both intelligible and free from contradiction; and (2) De jure, there should be nothing contingent at all, and indeed there would be nothing contingent in the absence of an overriding external cause (or reason), because that state of affairs is 'most natural' or 'normal'. The putative world containing nothing contingent is the so-called 'Null World'. As for (1), the logical robustness of the Null Possibility of there being nothing contingent needs to be demonstrated. But even if the Null Possibility is demonstrably genuine, there is an issue: Does that possibility require us to explain why it is not actualized by the Null World, which contains nothing contingent? And, as for (2), it originated as a corollary of the distinctly Christian precept (going back to the second century) that the very existence of any and every contingent entity is utterly dependent on God at any and all times. Like (1), (2) calls for scrutiny. Clearly, if either of these presuppositions of Leibniz's PEQ is ill founded or demonstrably false, then PEQ is aborted as a non-starter, because in that case, it is posing an ill-conceived question. In earlier writings (Grünbaum [2000], p. 5), I have introduced the designation 'SoN' for the ontological 'spontaneity of nothingness' asserted in presupposition (2) of PEQ. Clearly, in response to PEQ, (2) can be challenged by asking the counter-question, 'But why should there be nothing contingent, rather than something contingent?' Leibniz offered an a priori argument for SoN. Yet it will emerge that a priori defences of it fail, and that it has no empirical legitimacy either. Indeed physical cosmology spells an important relevant moral: As against any a priori dictum on what is the 'natural' status of the universe, the verdict on that status depends crucially on empirical evidence. Thus PEQ turns out to be a non-starter, because its presupposed SoN is ill founded! Hence PEQ cannot serve as a springboard for creationist theism. Yet Leibniz and the English theist Richard Swinburne offered divine creation ex nihilo as their answer to the ill-conceived PEQ. But being predicated on SoN, their cosmological arguments for the existence of God are fundamentally unsuccessful. The axiomatically topmost laws of nature (the 'nomology') in a scientific theory are themselves unexplained explainors, and are thus thought to be true as a matter of brute fact. But theists have offered a theological explanation of the specifics of these laws as having been willed or intended by God in the mode of agent causation to be exactly what they are. A whole array of considerations are offered in Section 2 to show that the proposed theistic explanation of the nomology fails multiply to transform scientific brute facts into specifically explained regularities. Thus, I argue for The Poverty of Theistic Cosmology in two major respects. (shrink)

The analyses of explanation and causal beliefs are heavily dependent on using probability functions as models of epistemic states. There are, however, several aspects of beliefs that are not captured by such a representation and which affect the outcome of the analyses. One dimension that has been neglected in this article is the temporal aspect of the beliefs. The description of a single event naturally involves the time it occurred. Some analyses of causation postulate that the cause must not occur (...) later than the effect. If we want this kind of causality it is easy to add the appropriate clause to ( CAUS ). An alternative is not to rule out backwards causation or causal loops a priori , but expect that ( CAUS ), via the properties of the contraction P C - , will result in the desired temporal relation between C and E . One way of ensuring this is to postulate that when the probability function P is contracted to P C - , the probabilities of all events that occurred before C remain the same in P C - as in P . This means that all beliefs about the history of events up to C are left unaltered in the construction of the hypothetical state of belief P C - . In conclusion, I hope to have shown that, in spite of these limitations, ( EXP ) and ( CAUS ) provide viable analyses of explanation and causality between single events for the case when epistemic states can be described by probability functions. I have also shown that the two analyses can be used to explicate the close connections between the two notions. These analyses reduce the problems of explanation and causality, hopefully in a non-circular way, to the problem of identifying contractions of states of belief. (shrink)

This paper is a defense of "explanatory pluralism" (i.e., the view that some events can be correctly explained in two distinct ways). To defend pluralism, I identify two distinct (but compatible) styles of explanation in paleobiology. The first approach ("actual sequence explanation") traces out the particular forces that affect each species. The second approach treats the trend as "passive" or "random" diffusion away from a boundary in morphological space. I argue that while these strategies are distinct, some trends are correctly (...) explained in both ways. Further, since neither strategy can be reduced or eliminated from paleobiology, we should accept that both strategies can provide correct explanations for a single trend. (shrink)

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...) a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda. (shrink)

This paper investigates the relations between causality and propensity. Aparticular version of the propensity theory of probability is introduced, and it is argued that propensities in this sense are not causes. Some conclusions regarding propensities can, however, be inferred from causal statements, but these hold only under restrictive conditions which prevent cause being defined in terms of propensity. The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is investigated. It is argued (...) that causal networks cannot be identified with Bayesian propensity networks, but that causal networks can be a valuable heuristic guide for the construction of Bayesian propensity networks. (shrink)

My purpose here is to elaborate the reasons I maintain that Salmon has not been completely successful in reporting the history of work on explanation. The most important limitation of his account is that it does not emphasize the critical necessity to embrace a suitable conception of probability in the development of the theory of probabilistic explanation.

Philip Kitcher and Wesley C. Salmon have edited an important anthology of new papers on scientific explanation, a central problem—possibly the central problem—in the theory of science. Their collection begins with a comprehensive essay by Salmon that attempts to trace the development of work on this issue from Hempel and Oppenheim to the present. The University of Minnesota Press has published this article as a separate volume, which it is promoting as “a definitive introduction” to this area of inquiry. Apart (...) from its preface and indices, the Salmon volume is identical with the original essay. (shrink)

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...) response to, we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems. (shrink)

A “contrastive explanation” explains not only why some event A occurred, but why A occurred as opposed to some alternative event B. Some philosophers argue that agents could only be morally responsible for their choices if those choices have contrastive explanations, since they would otherwise be “luck infested”. Assuming that contrastive explanations cannot be offered for causally undetermined events, this requirement entails that no one could be held responsible for a causally undetermined choice. Such arguments challenge incompatibilism, since they entail (...) that causal determinism is a prerequisite for moral responsibility. However, I argue that for a significant class of choices, even if we stipulate that they are determined, we will be unable to provide a relevant contrastive explanation. Hence causal determinism is no remedy for luck infestation, and compatibilists do not fare much better than incompatibilists in the face of this requirement. This should serve to weaken its philosophical appeal. (shrink)

Not all probability ascriptions that appear in scientific theories describe chances. There is a question about whether probability ascriptions in non-fundamental sciences, such as those found in evolutionary biology and statistical mechanics, describe chances in deterministic worlds and about whether there could be any chances in deterministic worlds. Recent debate over whether chance is compatible with determinism has unearthed two strategies for arguing about whether a probability ascription describes chance—that is, to speak metaphorically, two different strategies for figuring out where (...) the chances are: find the chances by focusing on chance’s explanatory role or find the chances by focusing on chance’s predictive role. These two strategies tend to yield conflicting results about where the chances are, and debate over which strategy is appropriate tends to end in stalemate. After discussing these two strategies, I consider a new view of chance’s explanatory role. I argue that one theoretical advantage of this new view is that allows us to make progress on the question of where the chances are by providing a principled way of determining which probability ascriptions describe chances. From the vantage of this new view, the correct application of both strategies involves figuring out where the chances are by figuring out where the probabilistic scientific explanations are and what those explanations are like. (shrink)

Much of the literature on "ceteris paribus" laws is based on a misguided egalitarianism about the sciences. For example, it is commonly held that the special sciences are riddled with ceteris paribus laws; from this many commentators conclude that if the special sciences are not to be accorded a second class status, it must be ceteris paribus all the way down to fundamental physics. We argue that the (purported) laws of fundamental physics are not hedged by ceteris paribus clauses and (...) provisos. Furthermore, we show that not only is there no persuasive analysis of the truth conditions for ceteris paribus laws, there is not even an acceptable account of how they are to be saved from triviality or how they are to be melded with standard scientific methodology. Our way out of this unsatisfactory situation to reject the widespread notion that the achievements and the scientific status of the special sciences must be understood in terms of ceteris paribus laws. (shrink)

This article is concerned with a statistical proposal due to James R. Beebe for how to solve the generality problem for process reliabilism. The proposal is highlighted by Alvin I. Goldman as an interesting candidate solution. However, Goldman raises the worry that the proposal may not always yield a determinate result. We address this worry by proving a dilemma: either the statistical approach does not yield a determinate result or it leads to trivialization, i.e. reliability collapses into truth (and anti-reliability (...) into falsehood). Various strategies for avoiding this predicament are considered, including revising the statistical rule or restricting its application to natural kinds. All amendments are seen to have serious problems of their own. We conclude that reliabilists need to look elsewhere for a convincing solution to the generality problem. (shrink)