Online research in philosophy

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture (conclusion) and thus serves as an abductive search strategy (Sect. 1). A classification of different patterns of abduction is provided which intends to be as complete as possible (Sect. 2). An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations (Sects. 3–4), and creative abductions, which introduce new theoretical models or concepts (Sects. 5–7). 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 (Sect. 7.1).

Abstract In Peirce's and Hanson's characterization of abductive inference, the abducted hypothesis (but not others) is present in the premises, so that the inference can hardly be taken as ampliative. Abduction has consequently been treated as part of the process whereby already generated hypotheses are judged in terms of their plausibility, simplicity, etc. I propose an interpretation of abduction which supports an ampliative view. It relies on a distinction between two logical stages in the generation of hypotheses, one ?factual? and one ?explanatory?. I also indicate how we may reconstruct Peirce's and Hanson's original inference in an ampliative form.

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 (the method of analysis), 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 (IBE) 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 (Smart, Putnam, Boyd), while the critics of realism have attempted to show that this appeal to abduction is question-begging, circular, or incoherent (Fine, Laudan, van Fraassen). 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.

Recent developments in the cognitive sciences and artificial intelligence suggest ways of answering the most serious challenge to Peirce's notion of abduction. Either there is no such logical process as abduction or, if abduction is a form of inference, it is essentially unconscious and therefore beyond rational control so that it lacks any normative significance. Peirce himself anticipates and attempts to answer this challenge. Peirce argues that abduction is both a source of creative insight and a form of logical inference subject to a degree of conscious control. In this paper I shall sketch a developing account of abduction that is suggested by the work of Paul Churchland, Paul Thagard, Chris Eliasmith, William Wimsatt, Owen Flanagan, and others. I shall argue that a credible account of abduction will require that we approach the phenomenon from both higher and lower levels as represented by these approaches.

It is well known that the process of scientific inquiry, according to Peirce, is drivenby three types of inference, namely abduction, deduction, and induction. What isbehind these labels is, however, not so clear. In particular, the common identificationof abduction with Inference to the Best Explanation (IBE) begs the question,since IBE appears to be covered by Peirce's concept of induction, not that of abduction.Consequently, abduction ought to be distinguished from IBE, at least on Peirce's account. The main aim of the paper, however, is to show that this distinction is most relevant with respect to current problems in philosophy of science and epistemology (like attempts to supply suitable notions of realism and truth as well as related concepts like coherence and unification). In particular, I also try to show that (and in what way) Peirce's inferential triad can function as a method that ensures both coherence and correspondence. It is in this respect that his careful distinction between abduction and induction (or IBE) ought to be heeded.

Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue that, with regard to the evaluation of hypotheses, Bayesian inductive inference is essentially a probabilistic form of induction by elimination. Bayesian induction is an extension of eliminativism to cases where, rather than contradict the evidence, false hypotheses imply that the evidence is very unlikely, much less likely than the evidence would be if some competing hypothesis were true. This is not, I think, how Bayesian induction is usually understood. The recent book by Howson and Urbach, for example, provides an excellent, comprehensive explanation and defense of the Bayesian approach; but this book scarcely remarks on Bayesian induction's eliminative nature. Nevertheless, the very essence of Bayesian induction is the refutation of false competitors of a true hypothesis, or so I will argue.

In this article, I compare two varieties of abduction as reconstructive models for analysing discovery. The first is 'Hansonian abduction', which is based on N. R. Hanson's formulations of abduction. The other is 'Harmanian abduction', the Inference to the Best Explanation (IBE) model, formulated especially by Gilbert Harman. Peter Lipton has analysed processes of discovery on the basis of his developed form of Harmanian abduction. I argue that Hansonian abduction would, however, be a more apt model for this purpose. As an example, I reconstruct, in a Hansonian manner, Ignaz Semmelweis's research on childbed fever and compare it to the IBE reconstruction of Lipton. I argue that Hansonian abduction is in accordance with Lipton's aim of taking into account the distinction between actual and potential explanations on the one hand, and between likely and lovely explanations on the other. I maintain that a developed version of Hansonian abduction combined with loveliness gives an important, new conceptual means for analysing processes of discovery.

I argue against the tendency in the philosophy of science literature to link abduction to the inference to the best explanation (IBE), and in particular, to claim that Peircean abduction is a conceptual predecessor to IBE. This is not to discount either abduction or IBE. Rather the purpose of this paper is to clarify the relation between Peircean abduction and IBE in accounting for ampliative inference in science. This paper aims at a proper classification—not justification—of types of scientific reasoning. In particular, I claim that Peircean abduction is an in-depth account of the process of generating explanatory hypotheses, while IBE, at least in Peter Lipton’s thorough treatment, is a more encompassing account of the processes both of generating and of evaluating scientific hypotheses. There is then a two-fold problem with the claim that abduction is IBE. On the one hand, it conflates abduction and induction, which are two distinct forms of logical inference, with two distinct aims, as shown by Charles S. Peirce; on the other hand it lacks a clear sense of the full scope of IBE as an account of scientific inference.

The usual, comparative, conception of inference to the best explanation (IBE) takes it to be ampliative. In this paper I propose a conception of IBE ('Holmesian inference') that takes it to be a species of eliminative induction and hence not ampliative. This avoids several problems for comparative IBE (for example, how could it be reliable enough to generate knowledge?). My account of Holmesian inference raises the suspicion that it could never be applied, on the grounds that scientific hypotheses are inevitably underdetermined by the evidence (i.e. are inevitably ampliative). I argue that this concern may be resisted by acknowledging, as Timothy Williamson has shown, that all knowledge is evidence. The latter suggests an approach to resisting scepticism different from those (e.g. the reliabilist approach) that embrace fallibilism.

The usual, comparative, conception of Inference to the Best Explanation (IBE) takes it to be ampliative. In this paper I propose a conception of IBE (‘Holmesian inference’) that takes it to be a species of eliminative induction and hence not ampliative. This avoids several problems for comparative IBE (e.g. how could it be reliable enough to generate knowledge?). My account of Holmesian inference raises the suspicion that it could never be applied, on the grounds that scientific hypotheses are inevitably underdetermined by the evidence (i.e. are inevitably ampliative). I argue that this concern may be resisted by acknowledging, as Timothy Williamson has shown, that all knowledge is evidence. This suggests an approach to resisting scepticism different from those (e.g. the reliabilist approach) that embrace fallibilism.