Ioannis G. Ntagkas

The demand that epistemic support be explicated as rational compulsion has consistently undermined the dialogue between theology and science. Rational compulsion entails too restrictive a form of epistemic support for most scientific theorizing, let alone interdisciplinary dialogue. This essay presents a less restrictive form of epistemic support, explicated not as rational compulsion but as explanatory power. Once this notion of epistemic support is developed, a genuinely productive interdisciplinary dialogue between theology and science becomes possible. This essay closes by sketching how the Big Bang model from cosmology and the Christian doctrine of Creation can be viewed as supporting each other.

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are explored.

Recently, in attempting to account for explanatory reasoning in probabilistic terms, Bayesians have proposed several measures of the degree to which a hypothesis explains a given set of facts. These candidate measures of "explanatory power" are shown to have interesting normative interpretations and consequences. What has not yet been investigated, however, is whether any of these measures are also descriptive of people’s actual explanatory judgments. Here, I present my own experimental work investigating this question. I argue that one measure in particular is an accurate descriptor of explanatory judgments. Then, I discuss some interesting implications of this result for both the epistemology and the psychology of explanatory reasoning.

This article generalizes the explanationist account of inference to the best explanation. It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence should be equated with explanatory power and considered to be derivable from the other explanatory virtues: unification, explanatory depth and simplicity

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

What we call “the evidential argument from evil” is not one argument but a family of them, originating (perhaps) in the 1979 formulation of William Rowe. Wykstra’s early versions of skeptical theism emerged in response to Rowe’s evidential arguments. But what sufficed as a response to Rowe may not suffice against later more sophisticated versions of the problem of evil—in particular, those along the lines pioneered by Paul Draper. Our chief aim here is to make an earlier version of skeptical theism more responsive to the type abductive atheology pioneered by Draper. In particular, we suggest a moderate form of skeptical theism may be able to resist Draper’s abductive atheology.

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

A theory’s fertility is one of the standard theoretical virtues. But how is it to be construed? In current philosophical discourse, particularly in the realism debate, theoretical fertility is usually understood in terms of novel success: a theory is fertile if it manages to make successful novel predictions. Another, more permissible, notion of fertility can be found in the work of Ernan McMullin. This kind of fertility, McMullin claims, gives us just as strong grounds for realism. My paper critically assesses McMullin’s notion of fertility and its realist rationale. It concludes that McMullin’s preferred example, namely the fertile development of the Bohr-Sommerfeld model of the atom, does not support McMullin’s argument for realism. Although the implications for the realism debate are as of yet unclear, the case study offers some important methodological lessons.

Philosophers use historical case studies to support wide-ranging claims about science. This practice is often criticized as problematic. In this paper we suggest that the function of case studies can be understood and justified by analogy to a well-established practice in biology: the investigation of model organisms. We argue that inferences based on case studies are no more problematic than inferences from model organisms to larger classes of organisms in biology. We demonstrate our view in detail by reference to a case study with a long history: Semmelweis’s discovery of the cause of childbed fever.

Proponents of the “negative program” in experimental philosophy have argued that judgements in philosophical cases, also known as case judgements, are unreliable and that the method of cases should be either strongly constrained or even abandoned. Here we put one of the main proponent’s account of why philosophical cases may cause the unreliability of case judgements to the test. We conducted our test with thought experiments from physics, which exhibit the exact same supposedly “disturbing characteristics” of philosophical cases.

What are the features of a good scientific theory? Samuel Schindler's book revisits this classical question in the philosophy of science and develops new answers to it. Theoretical virtues matter not only for choosing theories 'to work with', but also for what we are justified in believing: only if the theories we possess are good ones can we be confident that our theories' claims about nature are actually correct. Recent debates have focussed rather narrowly on a theory's capacity to predict new phenomena successfully, but Schindler argues that the justification for this focus is thin. He discusses several other theory properties such as testability, accuracy, and consistency, and highlights the importance of simplicity and coherence. Using detailed historical case studies and careful philosophical analysis, Schindler challenges the received view of theoretical virtues and advances arguments for the view that science uncovers reality through theory.

This book examines the evidential status and use of linguistic intuitions, a topic that has seen increased interest in recent years. Linguists use native speakers' intuitions - such as whether or not an utterance sounds acceptable - as evidence for theories about language, but this approach is not uncontroversial. The two parts of this volume draw on the most recent work in both philosophy and linguistics to explore the two major issues at the heart of the debate. Chapters in the first part address the 'justification question', critically analysing and evaluating the theoretical rationale for the evidential use of linguistic intuitions. The second part discusses recent developments in the domain of experimental syntax, focusing on the question of whether formal and systematic models of gathering intuitions are epistemically and methodologically superior to the informal methods that have traditionally been used. The volume provides valuable insights into whether and how linguistic intuitions can be used in theorizing about language, and will be of interest to graduate students and researchers in linguistics, philosophy, and cognitive science.

Few concepts have been considered as essential to the theory of knowledge and rational belief as that of evidence. The simplest theory which accounts for this is evidentialism, the view that epistemic justification for belief--the kind of justification typically taken to be required for knowledge--is determined solely by considerations pertaining to one's evidence. In this ground-breaking book, leading epistemologists from across the spectrum challenge and refine evidentialism, sometimes suggesting that it needs to be expanded in quite surprising directions. Following this, the twin pillars of contemporary evidentialism--Earl Conee and Richard Feldman--respond to each essay. This engaging debate covers a vast number of issues, and will illuminate and inform.

In the past twenty years, interest in the epistemic status of religious belief has greatly increased. Leading this revival are the philosophers Basil Mitchell and Richard Swinburne, who believe that {eligious belief can be justified using inductive "best explanation" arguments. However, while Swinburne's approach is formal, using the calculus of Bayes Theorem, Mitchell's is informal, based on his recognition of judgment as central to such an assessment. This book is the first full length comparison of these two men and their styles of justifying religious belief. The first half addresses the issues of rationality, endorsing Mitchell's methodology; the second half explores the concept of theistic explanation and demonstrates that the ontological argument has a place in the comprehensive explanatory power of theism.

I argue that there could not be grounds on which to introduce God into our ontology. My argument presupposes two doctrines. First, we should allow into our ontology only what figures in the best explanation of an event or fact. Second, explanation is contrastive by nature, in that the explanandum always consists in a contrast between a fact and a foil. I argue that God could not figure in true contrastive explanatory statements, because the omnipotence of God guarantees that for any true proposition p, God could have made it the case that ~p just as much as He could have made it the case that p.

How far should our realism extend? For many years philosophers of mathematics and philosophers of ethics have worked independently to address the question of how best to understand the entities apparently referred to by mathematical and ethical talk. But the similarities between their endeavours are not often emphasised. This book provides that emphasis. In particular, it focuses on two types of argumentative strategies that have been deployed in both areas. The first—debunking arguments—aims to put pressure on realism by emphasising the seeming redundancy of mathematical or moral entities when it comes to explaining our judgements. In the moral realm this challenge has been made by Gilbert Harman and Sharon Street; in the mathematical realm it is known as the 'Benacerraf-Field' problem. The second strategy—indispensability arguments—aims to provide support for realism by emphasising the seeming intellectual indispensability of mathematical or moral entities, for example when constructing good explanatory theories. This strategy is associated with Quine and Putnam in mathematics and with Nicholas Sturgeon and David Enoch in ethics. Explanation in Ethics and Mathematics addresses these issues through an explicitly comparative methodology which we call the 'companions in illumination' approach. By considering how argumentative strategies in the philosophy of mathematics might apply to the philosophy of ethics, and vice versa, the papers collected here break new ground in both areas. For good measure, two further companions for illumination are also broached: the philosophy of chance and the philosophy of religion. Collectively, these comparisons light up new questions, arguments, and problems of interest to scholars interested in realism in any area.

Epistemology has for a long time focused on the concept of knowledge and tried to answer questions such as whether knowledge is possible and how much of it there is. Often missing from this inquiry, however, is a discussion on the value of knowledge. In The Value of Knowledge and the Pursuit of Understanding Jonathan Kvanvig argues that epistemology properly conceived cannot ignore the question of the value of knowledge. He also questions one of the most fundamental assumptions in epistemology, namely that knowledge is always more valuable than the value of its subparts. Taking Platos' Meno as a starting point of his discussion, Kvanvig tackles the different arguments about the value of knowledge and comes to the conclusion that knowledge is less valuable than generally assumed. Clearly written and well argued, this 2003 book will appeal to students and professionals in epistemology.

According to Sir Karl Popper, there is a harmful approach to the social sciences called 'historicism'. This takes their principal aim to be historical prediction of an unconditional sort and the chief means to this the discovery of laws of historical development. The chief exemplar is held to be Marx. This paper distinguishes two possible sorts of laws of historical development. Popper's arguments against each are rejected. Which sort it is most plausible to ascribe to Marx is considered. Four models of historical determinism are distinguished and the question of Marx's view or views on the question is discussed in terms of them.

This Element has two main aims. The first one is an historically informed review of the philosophy of probability. It describes recent historiography, lays out the distinction between subjective and objective notions, and concludes by applying the historical lessons to the main interpretations of probability. The second aim focuses entirely on objective probability, and advances a number of novel theses regarding its role in scientific practice. A distinction is drawn between traditional attempts to interpret chance, and a novel methodological study of its application. A radical form of pluralism is then introduced, advocating a tripartite distinction between propensities, probabilities and frequencies. Finally, a distinction is drawn between two different applications of chance in statistical modelling which, it is argued, vindicates the overall methodological approach. The ensuing conception of objective probability in practice is the 'complex nexus of chance'.

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh.