This paper details how ghost hunting, as a set of learning activities, can be used to enhance critical thinking and philosophy of science classes. We describe in some detail our own work with ghost hunting, and reflect on both intended and unintended consequences of this pedagogical choice. This choice was partly motivated by students’ lack of familiarity with science and philosophic questions about it. We offer reflections on our three different implementations of the ghost hunting activities. In addition, we discuss (...) the practical nuances of implementing these activities, as well the relation of ghost hunting to our course content, including informal fallacies and some models for scientific inference. We conclude that employing ghost hunting along-side traditional activities and content of critical thinking and philosophy of science offers a number of benefits, including being fun, increasing student attendance, enhancing student learning, and providing a platform for campus wide dialogues about philosophy. (shrink)

In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing explanations—have confirmational import. (...) Rather, some of the items that feature on these lists are “informational virtues”—properties that do not make a hypothesis \ more probable than some competitor \ given evidence E, but that, roughly-speaking, give that hypothesis greater informative content. In Sect. 4, I consider as a response to my argument a recent version of compatibilism which argues that IBE can provide further normative constraints on the objectively correct probability function. I argue that this response does not succeed, owing to the difficulty of defending with any generality such further normative constraints. Lastly, in Sect. 5, I propose that IBE should be regarded, not as a theory of scientific inference, but rather as a theory of when we ought to “accept” H, where the acceptability of H is fixed by the goals of science and concerns whether H is worthy of commitment as research program. In this way, IBE and Bayesianism, as I will show, can be made compatible, and thus the Bayesian and the proponent of IBE can be friends. (shrink)

I defend an account of explanatory depth according to which explanations in the non-fundamental sciences can be deeper than explanations in fundamental physics.

In this book Martin Bunzl considers the prospects for a general and comprehensive account of explanation, given the variety of interests that prompt explanations in science. Bunzl argues that any successful account of explanation must deal with two very different contexts - one static and one dynamic. Traditionally, theories of explanation have been built for the former of these two contexts. That is to say, they are designed to show how it is that a 'finished' body of scientific knowledge can (...) be put to explanatory use. But finished sciences are few and far between. Real 'explanation' also occurs in a dynamical context in which questions are asked and answers are given as theories are in the process of being constructed. Here, Bunzl argues that attending to explanation produced under these dynamic circumstances undermines prominent features of the theory of explanation produced in the traditional static context. (shrink)

The Quine/Putnam indispensability approach to the confirmation of mathematical theories in recent times has been the subject of significant criticism. In this paper I explore an alternative to the Quine/Putnam indispensability approach. I begin with a van Fraassen-like distinction between accepting the adequacy of a mathematical theory and believing in the truth of a mathematical theory. Finally, I consider the problem of moving from the adequacy of a mathematical theory to its truth. I argue that the prospects for justifying this (...) move are qualitatively worse in mathematics than they are in science. (shrink)

Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. (...) Finally, we illustrate TETRAD's effectiveness by applying it to a multiple indicator model of Political and Industrial development. A pilot version of the TETRAD program is described in this paper. The current version is described in our forthcoming Discovering Causal Structure: Artificial Intelligence for Statistical Modeling. (shrink)

This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables St and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions (...) in the systems to which T applies or the magnitudes of the conditional probabilities P(Mj/Si), in the manner in which Ir does. (shrink)

This paper examines a recent attempt by Evan Jobe to account for the asymmetric character of many scientific explanations. It is argued that a purported counterexample to Jobe's account, from Clark Glymour, is inconclusive, but that the account faces independent objections. It is also suggested, contrary to Jobe, that the explanatory relation is not always asymmetric. Sometimes a singular sentence C can figure in a DN derivation of another singular sentence E and E can also figure in a DN derivation (...) of C. Yet while we are inclined to regard the first derivation as an explanation of E, we are not inclined to regard the second derivation as an explanation of C. As Sylvain Bromberger pointed out in a now classic article (1966), one can explain the period of a pendulum by reference to its length and yet, although one can give a DN derivation of the length of a pendulum by reference to its period, this derivation does not seem to represent an explanation. Evan Jobe has recently offered an interesting account of such explanatory asymmetries and Clark Glymour has in turn proposed a counterexample which seems to show that Jobe's account is defective. The aim of this paper is two-fold. I shall attempt to show that (a) Glymour's proposed counterexample can be rejected on the grounds that it violates an independently plausible restriction on the role that equalities may play in DN explanation, and that (b) although Glymour's counterexample can be avoided in this way, Jobe's account is defective in several other respects. (shrink)

Van Fraassen argues that explanatory power cannot be a conformational virtue. In this paper I will show that informational features of scientific theories can be positively relevant to their levels of conformation. Thus, in the cases where the explanatory power of a theory is tied to an informational feature of the theory, it can still be the case that the explanatory power of the theory is positively relevant to its level of confirmation.

The likelihood theory of evidence (LTE) says, roughly, that all the information relevant to the bearing of data on hypotheses (or models) is contained in the likelihoods. There exist counterexamples in which one can tell which of two hypotheses is true from the full data, but not from the likelihoods alone. These examples suggest that some forms of scientific reasoning, such as the consilience of inductions (Whewell, 1858. In Novum organon renovatum (Part II of the 3rd ed.). The philosophy of (...) the inductive sciences. London: Cass, 1967), cannot be represented within Bayesian and Likelihoodist philosophies of science. (shrink)

The recent literature in philosophy of biology has drawn attention to the different sorts of explanations proffered in the biological sciences—we have molecular, biomedical, and evolutionary explanations. Do these explanations all have a common structure or relation that they seek to capture? This paper will answer in the negative. I defend a pluralistic and pragmatic approach to explanation. Using examples from classical population genetics, I argue that formal demonstrations, and even strictly “mathematical truths,” may serve as explanatory in different historical (...) contexts. (shrink)

An amended bootstrapping can avoid Christensen's counterexamples. Earman and Edidin argue that Christensen's examples to bootstrapping rely on his failure to analyze background knowledge. I add an additional condition to bootstrapping that is motivated by Glymour's remarks on variety of evidence. I argue that it avoids the problems that the examples raise. I defend the modification against the charge that it is holistic, and that it collapses into Bayesianism.

Tenenbaum and Griffiths (Behavioral and Brain Sciences 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (Science 237(4820): 1317–1323, 1987) and Tversky’s (Psychological Review 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that (...) generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

Filósofos, especialmente aqueles que se autodenominam como naturalistas, parecem cada vez mais interessados em realizar pesquisas empíricas. Um caso típico é o da Filosofia Experimental, um campo emergente que faz uso de dados empíricos colhido por meio de inquéritos seguindo os mesmos métodos empregados nas ciências empíricas, nomeadamente na psicologia, a fim de apresentar provas em pesquisas ou argumentos filosóficos. Outro exemplo é oferecido pela participação ativa dos filósofos em grupos de pesquisa interdisciplinares nas neurociências. No entanto, os filósofos não (...) são especialistas em metodologia de pesquisa empírica. Isso é transparente nos estudos mais representativos em filosofia experimental. Neste artigo, faremos algumas observações de advertência sobre as complexidades da metodologia das ciências empíricas. A recomendação final é de que é sábia a atitude de envolver-se em grupos de pesquisa interdisciplinares, buscando dominar as peculiaridades dos mais diversos delineamentos de pesquisa empírica nas diferentes áreas da ciência. (shrink)

Philosophers of science have long been concerned with these questions. In the 1980s, influential work by Clark Glymour, Michael Friedman, John Watkins, and Philip Kitcher articulated general accounts of theory unification that attempted to underwrite a connection between unification, truth, and understanding. According to the ‘unifiers,’ as we may call them, a theory is unified to the extent that it has a small theoretical structure relative to the domain of phenomena it covers, and there are general syntactic criteria that allow (...) one to determine how unified a theory is. The explanatory power of a theory, and the understanding of nature it gives us, is a direct consequence of this unity. As well, the more unified a theory is, the better confirmed it will be, and under some conditions a theory’s unity can justify realism about unobservable entities posited by it. In the 1990s disunity became the dominant theme, with books such as John Dupré’s The Disorder of Things and the Galison and Stump anthology arguing that it is a mistake to view science as a unified practice and that rather than an epistemic virtue, unification in science is a metaphysical vice. (shrink)

This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, (...) we briefly describe some promising future projects inspired by our account. (shrink)

Huxley coined the phrase, the “evolutionary synthesis” to refer to the acceptance by a vast majority of biologists in the mid-20th Century of a “synthetic” view of evolution. According to this view, natural selection acting on minor hereditary variation was the primary cause of both adaptive change within populations and major changes, such as speciation and the evolution of higher taxa, such as families and genera. This was, roughly, a synthesis of Mendelian genetics and Darwinian evolutionary theory; it was a (...) demonstration that prior barriers to understanding between various subdisciplines in the life sciences could be removed. The relevance of different domains in biology to one another was established under a common research program. The evolutionary synthesis may be broken down into two periods, the “early” synthesis from 1918 through 1932, and what is more often called the “modern synthesis” from 1936-1947. The authors most commonly associated with the early synthesis are J.B.S. Haldane, R.A. Fisher, and S. Wright. These three figures authored a number of important synthetic advances; first, they demonstrated the compatibility of a Mendelian, particulate theory of inheritance with the results of Biometry, a study of the correlations of measures of traits between relatives. Second, they developed the theoretical framework for evolutionary biology, classical population genetics. This is a family of mathematical models representing evolution as change in genotype frequencies, from one generation to the next, as a product of selection, mutation, migration, and drift, or chance. Third, there was a broader synthesis of population genetics with cytology, genetics, and biochemistry, as well as both empirical and mathematical demonstrations to the effect that very small selective forces acting over a relatively long time were able to generate substantial evolutionary change, a novel and surprising result to many skeptics of Darwinian gradualist views. The later “modern” synthesis is most often identified with the work of Mayr, Dobzhansky and Simpson. There was a major institutional change in biology at this stage, insofar as different subdisciplines formerly housed in different departments, and with different methodologies were united under the same institutional umbrella of “evolutionary biology.” Mayr played an important role as a community architect, in founding the Society for the Study of Evolution, and the journal Evolution, which drew together work in systematics, biogeography, paleontology, and theoretical population genetics. (shrink)