Two chief problems of the theory of knowledge are the question of meaning and the question of verification. The first question asks under what conditions a sentence has meaning, in the sense of cognitive, factual meaning. The second one asks how we get to know something, how we can find out whether a given sentence is true or false. The second question presupposes the first one. Obviously we must understand a sentence, i.e. we must know its meaning, before we can (...) try to find out whether it is true or not. But, from the point of view of empiricism, there is a still closer connection between the two problems. In a certain sense, there is only one answer to the two questions. If we knew what it would be for a given sentence to be found true then we would know what its meaning is. And if for two sentences the conditions under which we would have to take them as true are the same, then they have the same meaning. Thus the meaning of a sentence is in a certain sense identical with the way we determine its truth or falsehood; and a sentence has meaning only if such a determination is possible. (shrink)

Analyses of dispositional ascriptions in terms of conditional statements famously confront the problems of finks and masks. We argue that conditional analyses of dispositions, even those tailored to avoid finks and masks, face five further problems. These are the problems of: (i) Achilles' heels, (ii) accidental closeness, (iii) comparatives, (iv) explaining context sensitivity, and (v) absent stimulus conditions. We conclude by offering a proposal that avoids all seven of these problems.

Two chief problems of the theory of knowledge are the question of meaning and the question of verification. The first question asks under what conditions a sentence has meaning, in the sense of cognitive, factual meaning. The second one asks how we get to know something, how we can find out whether a given sentence is true or false. The second question presupposes the first one. Obviously we must understand a sentence, i.e. we must know its meaning, before we can (...) try to find out whether it is true or not. But, from the point of view of empiricism, there is a still closer connection between the two problems. In a certain sense, there is only one answer to the two questions. If we knew what it would be for a given sentence to be found true then we would know what its meaning is. And if for two sentences the conditions under which we would have to take them as true are the same, then they have the same meaning. Thus the meaning of a sentence is in a certain sense identical with the way we determine its truth or falsehood; and a sentence has meaning only if such a determination is possible. (shrink)

It is not the aim of the present essay to defend the principle of empiricism against apriorism or anti-empiricist metaphysics. Taking empirism for granted, we wish to discuss, the question what is meaningful. The word ‘meaning’ will here be taken in its empiricist sense; an expression of language has meaning in this sense if we know how to use it in speaking about empirical facts, either actual or possible ones. Now our problem is what expressions are meaningful in this sense. (...) We may restrict this question to sentences because expressions other than sentences are meaningful if and only if they can occur in a meaningful sentence. (shrink)

Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. However, the exact form as well as the normative status of abduction are still matters of controversy. This entry contrasts abduction with other types of inference; points at prominent uses of it, both in and outside philosophy; considers various more or less precise statements of it; (...) discusses its normative status; and highlights possible connections between abduction and Bayesian confirmation theory. (shrink)

In three recent papers, Wayne Myrvold and Timothy McGrew have developed Bayesian accounts of the virtue of unification. In his account, McGrew demonstrates that, ceteris paribus, a hypothesis that unifies its evidence will have a higher posterior probability than a hypothesis that does not. Myrvold, on the other hand, offers a specific measure of unification that can be applied to individual hypotheses. He argues that one must account for this measure in order to calculate correctly the degree of confirmation that (...) a hypothesis receives from its evidence. Using the probability calculus, I prove that the two accounts of unification require the same underlying inequality; thus, McGrew and Myrvold have accounted for unification in fundamentally identical probabilistic terms. I then evaluate five putative counterexamples to this account and show that these examples, far from disqualifying it, serve to clarify our notion of unification by disentangling it from a host of other concepts. (shrink)

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...) literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification. (shrink)

Charles S. Peirce argued that, besides deduction and induction, there is a third mode of inference which he called " hypothesis " or " abduction." He characterized abduction as reasoning " from effect to cause," and as " the operation of adopting an explanatory hypothesis." Peirce ' s ideas about abduction, which are related also to historically earlier accounts of heuristic reasoning, have been seen as providing a logic of scientific discovery. Alternatively, abduction is interpreted as giving reasons for pursuing (...) a hypothesis. Inference to the best explanation has also been regarded as an important mode of justification, both in everyday life, detective stories, and science. In particular, scientific realism has been defended by an abductive nomiracle argument, while the critics of realism have attempted to show that this appeal to abduction is question - begging, circular, or incoherent. This paper approaches these issues by distinguishing weaker and stronger forms of abduction, and by showing how these types of inferences can be given Peircean and Bayesian probabilistic reconstructions. (shrink)

Many years ago, C.B. Martin drew our attention to the possibility of ‘finkish’ dispositions: dispositions which, if put to the test would not be manifested, but rather would disappear. Thus if x if finkishly disposed to give response r to stimulus s, it is not so that if x were subjected to stimulus r, x would give response z; so finkish dispositions afford a counter‐example to the simplest conditional analysis of dispositions. Martin went on to suggest that finkish dispositions required (...) a theory of primitive causal powers; there, I think, he was mistaken. All that they require is an improved conditional analysis, and this improved analysis can be built upon whatever treatments of properties and of laws we may favour on other grounds. (shrink)

The ultimate focus of the current essay is on methods of “creative abduction” that have some guarantees as reliable guides to the truth, and those that do not. Emphasizing work by Richard Englehart using data from the World Values Survey, Gerhard Schurz has analyzed literature surrounding Samuel Huntington’s well-known claims that civilization is divided into eight contending traditions, some of which resist “modernization” – democracy, civil rights, equality of rights of women and minorities, secularism. Schurz suggests an evolutionary model of (...) modernization and identifies opposing social forces. In a later essay, citing Englehart’s work as an example, Schurz identifies factor analysis as an example of “creative abduction”. The theories of Englehart and his collaborators are reviewed again in the current essay. Published simulations and standard statistical desiderata for causal inference show the methods Englehart used, factor analysis in particular, are not guides to truth for the kind of data Schurz recognizes as common in political science. Recent work in statistics, philosophy and computer science that makes advances towards such methods is briefly reviewed. (shrink)

An important distinction is that between selective abductions, which select an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical concepts and models. The article focuses on creative abductions, which are essential for scientific progress, although they are rarely discussed in the literature. Scientifically, fruitful creative abductions are demarcated from purely speculative abductions by means of three virtues which are possessed by the former but not by the latter: (i) providing unification, (ii) detecting common (...) causes and (iii) generating novel predictions by means of which they can be independently tested. Based on historical examples it is demonstrated that common cause abduction from correlated dispositions is the fundamental abductive operation by which new theoretical concepts and models are scientifically generated. Statistical factor analysis can be regarded as a statistical generalization of common cause abduction. (shrink)

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal nets and has been explicitly promoted by Glymour. In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause–effect relations can be (...) philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content. (shrink)

We present a Bayesian analysis of the epistemology of analogue experiments with particular reference to Hawking radiation. Provided such experiments can be externally validated via universality arguments, we prove that they are confirmatory in Bayesian terms. We then provide a formal model for the scaling behaviour of the confirmation measure for multiple distinct realisations of the analogue system and isolate a generic saturation feature. Finally, we demonstrate that different potential analogue realisations could provide different levels of confirmation. Our results thus (...) provide a basis both to formalise the epistemic value of analogue experiments that have been conducted and to advise scientists as to the respective epistemic value of future analogue experiments. (shrink)

This monograph looks at causal nets from a philosophical point of view. The author shows that one can build a general philosophical theory of causation on the basis of the causal nets framework that can be fruitfully used to shed new light on philosophical issues. Coverage includes both a theoretical as well as application-oriented approach to the subject. The author first counters David Hume’s challenge about whether causation is something ontologically real. The idea behind this is that good metaphysical concepts (...) should behave analogously to good theoretical concepts in scientific theories. In the process, the author offers support for the theory of causal nets as indeed being a correct theory of causation. Next, the book offers an application-oriented approach to the subject. The author shows that causal nets can investigate philosophical issues related to causation. He does this by means of two exemplary applications. The first consists of an evaluation of Jim Woodward’s interventionist theory of causation. The second offers a contribution to the new mechanist debate. Introductory chapters outline all the formal basics required. This helps make the book useful for those who are not familiar with causal nets, but interested in causation or in tools for the investigation of philosophical issues related to causation. (shrink)

We have examined only a few of the basic questions about causal inference that result from Reichenbach's two principles. We have not considered what happens when the probability distribution is a mixture of distributions from different causal structures, or how unmeasured common causes can be detected, or what inferences can reliably be drawn about causal relations among unmeasured variables, or the exact advantages that experimental control offers. A good deal is known about these questions, and there is a good deal (...) more to find out. (shrink)

Two key ideas of scientific explanation - explanations as causal information and explanation as unification - have frequently been set into mutual opposition. This paper proposes a "dialectical solution" to this conflict, by arguing that causal explanations are preferable to non-causal explanations because they lead to a higher degree of unification at the level of the explanation of statistical regularities. The core axioms of the theory of causal nets are justified because they give the best if not the only unifying (...) explanation of two statistical phenomena: screening off and linking up. Alternative explanation attempts are discussed and it is shown why they don't work. It is demonstrated that not the core of TC but extended versions of TC have empirical content, by means of which they can generate independently testable predictions. (shrink)

Two key ideas of scientific explanation−explanation as causal information and explanation as unification-have frequently been set into mutual opposition. This paper proposes a “dialectical solution” to this conflict, by arguing that causal explanations are preferable to non-causal ones, because they lead to a higherdegree of unification at the level of explaining statistical regularities. The core axioms of the theory of causal nets (TC) are justified because they offer the best if not the only unifying explanation of two statistical phenomena: screening (...) off and linking up. Alternative explanations of the two phenomena are discussed and it isshown why they don’t work. It is demonstrated that although the core axioms of TC are empirically vacuous, extended versions of TC have empirical content by means of which they can generate independently testable predictions.Con frecuencia se han planteado como contrapuestas dos ideas clave en la explicacion cientifica (explicacion como informacion causal y explicacion como unificacion). El presente articulo propone una “solucion dialectica” argumentando que las explicaciones causales son preferibles a las no-causales porque aquellas comportan un mayor grado de unificacion en la explicacion de regularidades estadisticas. Los axiomas centrales de la teoria de redes causales (TC) estan justificados porque ofrecen la mejor, si no la unica, explicacion unificada de dos fenomenos estadisticos: neutralizacion (screening off) y vinculacion (linking up). Se discuten las explicaciones alternativas de estos dos fenomenos y se razona por que no funcionan. Se demuestra ademas que aunque los axiomas centrales de TC son empiricamente vacuos, las versiones extendidas de TC tienen un contenido empirico gracias al cual pueden generar predicciones independientemente contrastables. (shrink)

In this paper, I explore Larry Laudan's and Jarrett Leplin's recent claim that empirically equivalent theories may be differentially confirmed. I show that their attempt to prise apart empirical equivalence and epistemic parity commits them to two principles of confirmation that Hempel demonstrated to be incompatible.

This paper discusses two senses in which a hypothesis may be said to unify evidence. One is the ability of the hypothesis to increase the mutual information of a set of evidence statements; the other is the ability of the hypothesis to explain commonalities in observed phenomena by positing a common origin for them. On Bayesian updating, it is only mutual information unification that contributes to the incremental support of a hypothesis by the evidence unified. This poses a challenge for (...) the view that explanation is a confirmatory virtue that contributes to such incremental support; its advocates must ground it in some relevant difference between humans and a Bayesian agent. (shrink)