Abduction* is the genus with deduction and induction as species. Modus tollens is backward reasoning as an unknown proposition is inferred from a known proposition. Reductio ad absurdum is abductive because the conclusion is inferred by deriving a contradiction from an assumption. Inductive reasoning from effect to cause is also backward reasoning. But abduction* consists of forward reasoning as well. The generic structure of abductive* argumentation is universal among all cultures, occupations and disciplines.

Peirce believed that inquiry involves three types of reasoning—abduction, deduction, and induction. While Peirce’s beliefs about reasoning, especially abduction, changed over time, in his mature work the following picture of reasoning emerges: abduction generates and chooses hypotheses to test; deduction determines the entailments of a hypothesis; induction ascertains whether the evidence accords with the hypothesis in question.1 Peirce both identified abduction and coined the word.2 His concept of abduction is one of the most original contributions he made to (...) the study of reasoning. It is also one of the most poorly understood. Peirce’s concept of abduction has been distorted by contemporary... (shrink)

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 Peireean 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 Peireean 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 Peireean 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. (shrink)

Many epistemologists take Inference to the Best Explanation (IBE) to be “fundamental.” For instance, Lycan (1988, 128) writes that “all justified reasoning is fundamentally explanatory reasoning.” Conee and Feldman (2008, 97) concur: “fundamental epistemic principles are principles of best explanation.” Call them fundamentalists. They assert that nothing deeper could justify IBE, as is typically assumed of rules of deductive inference, such as modus ponens. However, logicians account for modus ponens with the valuation rule for the material (...) conditional. By contrast, fundamentalists account for IBE with an ill-defined set of relations that happen to furnish their favorite set of inductive inferences. To our eye, this seems a little too convenient—there is too much room for ad hoc, just-so stories about the “striking” correspondence between our explanatory and inductive practices. We will argue that the (explanatory) pluralism adopted by the leading theorists of the best explanation—philosophers of science—undermines fundamentalism. Section 1 clarifies fundamentalism’s key tenets. Section 2 presents pluralism’s challenge to fundamentalism. Section 3 considers a potential fundamentalist reply to this challenge. Sections 4 through 6 canvass the leading candidates for developing this fundamentalist reply, showing each to be unsatisfactory. (shrink)

Inference to the best explanation (IBE) is considered to be the main means of discovery and justification of scientific hypotheses and theories. The article investigates this inference and its relationship to the main kinds of inference: deduction, induction and abduction. IBE has an abductive inference mechanism, but, contrary to abduction, infers not a possible, but a true conclusion. IBE is an inductive inference, because it is underdetermined by the rules of deduction and by (...) evidence. The article claims that despite its abductive and inductive features which demonstrate that it is not and cannot be deductive inference, IBE nevertheless makes pretense to an absolute truth of its inference, i.e. claims for an almost deductive validity. (shrink)

Going back at least to Duhem, there is a tradition of thinking that crucial experiments are impossible in science. I analyse Duhem's arguments and show that they are based on the excessively strong assumption that only deductive reasoning is permissible in experimental science. This opens the possibility that some principle of inductive inference could provide a sufficient reason for preferring one among a group of hypotheses on the basis of an appropriately controlled experiment. To be sure, there are analogues (...) to Duhem's problems that pertain to inductive inference. Using a famous experiment from the history of molecular biology as an example, I show that an experimentalist version of inference to the best explanation (IBE) does a better job in handling these problems than other accounts of scientific inference. Furthermore, I introduce a concept of experimental mechanism and show that it can guide inferences from data within an IBE-based framework for induction. 1. Introduction2. Duhem on the Logic of Crucial Experiments3. ‘The Most Beautiful Experiment in Biology’4. Why Not Simple Elimination?5. Severe Testing6. An Experimentalist Version of IBE 6.1. Physiological and experimental mechanisms6.2. Explaining the data6.3. IBE and the problem of untested auxiliaries6.4. IBE-turtles all the way down7. Van Fraassen's ‘Bad Lot’ Argument8. IBE and Bayesianism9. Conclusions. (shrink)

Hintikka argues that abduction is ignorance-preserving in the sense that the hypothesis that abduction delivers and which attempts to explain a set of phenomena is not, epistemologically speaking, on a firmer ground than the phenomena it purports to explain; knowledge is not enhanced until the hypothesis undergoes a further inductive process that will test it against empirical evidence. Hintikka, therefore, introduces a wedge between the abductive process properly speaking and the inductive process of hypothesis testing. Similarly, Minnameier argues that abduction (...) differs from the inference to the best explanation since the former describes the process of generation of theories, while the latter describes the, inductive, process of their evaluation. As Hintikka so Minnameier traces this view back to Peirce’s work on abduction. Recent work on abduction goes as far as to draw a distinction between abducting an hypothesis that is considered worth conjecturing and the decision either to use further this hypothesis to do some inferential work in the given domain of enquiry, or to test it experimentally. The latter step, when it takes place, is an inductive mode of inference that should be distinguished from the abductive inference that led to the hypothesis. In this paper, I argue that in real scientific practise both the distinction between a properly speaking abductive phase and an inductive phase of hypothesis testing and evaluation, and the distinction between testing an hypothesis that has been discovered in a preceding abduction and releasing or activating the same hypothesis for further inferential work in the domain of enquiry in which the ignorance problem arose in the first place are blurred because all these processes form an inextricable whole of theory development and elaboration and this defies and any attempt to analyze this intricate process into discrete well defined steps. Thus, my arguments reinforce Magnani’s view on abduction and its function in scientific practise. (shrink)

In this article, I will provide a critical overview of the form of non-deductive reasoning commonly known as “Inference to the Best Explanation” (IBE). Roughly speaking, according to IBE, we ought to infer the hypothesis that provides the best explanation of our evidence. In section 2, I survey some contemporary formulations of IBE and highlight some of its putative applications. In section 3, I distinguish IBE from C.S. Peirce’s notion of abduction. After underlining some of the essential (...) elements of IBE, the rest of the entry is organized around an examination of various problems that IBE confronts, along with some extant attempts to address these problems. In section 4, I consider the question of when a fact requires an explanation, since presumably IBE applies only in cases where some explanation is called for. In section 5, I consider the difficult question of how we ought to understand IBE in light of the fact that among philosophers, there is significant disagreement about what constitutes an explanation. In section 6, I consider different strategies for justifying the truth-conduciveness of the explanatory virtues, e.g., simplicity, unification, scope, etc., criteria which play an indispensable role in any given application of IBE. In section 7, I survey some of the most recent literature on IBE, much of which consists of investigations of the status of IBE from the standpoint of the Bayesian philosophy of science. (shrink)

The analysis of theory-confirmation generally takes the deductive form: show that a theory in conjunction with physical data and auxiliary hypotheses yield a prediction about phenomena; verify the prediction; provide a quantitative measure of the degree of theory-confirmation this yields. The issue of confirmation for an entire framework (e.g., Newtonian mechanics en bloc, as opposed, say, to Newton’s theory of gravitation) either does not arise, or is dismissed in so far as frameworks are thought not to be the kind of (...) thing that admits scientific confirmation. I argue that there is another form of scientific reasoning that has not received philosophical attention, what I call Newtonian abduction, that does provide confirmation for frameworks as a whole, and does so in two novel ways. (In particular, Newtonian abduction is not inference to the best explanation, but rather is closer to Peirce’s original idea of abduction.) I further argue that Newtonian abduction is at least as important a form of reasoning in science as standard deductive and inductive forms. The form is beautifully summed up by Maxwell (Proc Camb Philos Soc II:292–294, 1876): “The true method of physical reasoning is to begin with the phenomena and to deduce the forces from them by a direct application of the equations of motion.”. (shrink)

In this paper I want to cast doubt on the claim that there is a legitimate process of reasoning to the best explanation which can serve as an alternative to either straightforward inductive reasoning or a combination of inductive and deductive reasoning. I shall argue a) that paradigmatic cases of acceptable arguments to the best explanation must be considered enthymemes and b) that when the suppressed premises are made explicit we have all of the premises we need to (...) present either a straightforward inductive argument or an argument employing both induction and deduction. (shrink)

An interesting consequence of the structuralist theory of truth approximation, as developed in my From Instrumentalism to Constructive Realism , henceforth ICR, concerns so-called ‘inference to the best explanation’ . It will be argued that this popular rule among scientific realists can better be replaced by, various kinds of, ‘inference to the best theory’.

This article discusses how inference to the best explanation can be justified as a practical meta - argument. It is, firstly, justified as a practical argument insofar as accepting the best explanation as true can be shown to further a specific aim. And because this aim is a discursive one which proponents can rationally pursue in — and relative to — a complex controversy, namely maximising the robustness of one’s position, IBE can be conceived, secondly, as a (...) meta - argument. My analysis thus bears a certain analogy to Sellars ’ well - known justification of inductive reasoning ; it is based on recently developed theories of complex argumentation. (shrink)

The common role of research programs in science and religion is now widely accepted. The next step in the methodology debate is to specify more concretely the shared standards for adequate explanations. The article presents a detailed account of the method of inference to the best explanation and gives examples of how the method can structure the philosophical and theological interaction with science. The resulting approach dispenses with deductive and inductive proofs of religious propositions and limits itself to (...) initially plausible hypotheses that are to be assessed according to their explanatory power. Only when a domain of data and a particular explanatory task have been specified can any serious claim be made that religious theories are equal or superior to their naturalistic alternatives. (shrink)

Inference to the Best Explanation (IBE) is widely criticized for being an unreliable form of ampliative inference – partly because the explanatory hypotheses we have considered at a given time may all be false, and partly because there is an asymmetry between the comparative judgment on which an IBE is based and the absolute verdict that IBE is meant to license. In this paper, I present a further reason to doubt the epistemic merits of IBE and argue (...) that it motivates moving to an inferential pattern in which IBE emerges as a degenerate limiting case. Since this inferential pattern is structurally similar to an argumentative strategy known as Inferential Robustness Analysis (IRA), it effectively combines the most attractive features of IBE and IRA into a unified approach to non-deductive inference. (shrink)

This paper argues that in mathematical practice, conjectures are sometimes confirmed by “Inference to the Best Explanation” as applied to some mathematical evidence. IBE operates in mathematics in the same way as IBE in science. When applied to empirical evidence, IBE sometimes helps to justify the expansion of scientists’ ontological commitments. Analogously, when applied to mathematical evidence, IBE sometimes helps to justify mathematicians' in expanding the range of their ontological commitments. IBE supplements other forms of non-deductive reasoning in (...) mathematics, avoiding obstacles sometimes faced by enumerative induction or hypothetico-deductive reasoning. Both platonist and non-platonist interpretations of mathematics ought to accommodate explanation in mathematics and ought to recognize IBE in mathematics, though these interpretations disagree on the ontological commitments that mathematicians ought to have. This paper offers an inductive account of why mathematical IBE tends to lead to mathematical truths. (shrink)

In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents (...) new ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science. (shrink)

Selective abduction is in contrast with creative abduction as well as Inference to the Best Explanation (IBE). There are two types of selective abduction: Either hypotheses are selected among new and conjectural hypotheses without any prior knowledge ( Pierce s' selective abduction), or the selection of the best hypotheses and explanations is among a large number of possible hypotheses and explanations already known (L. Magnani's selective abduction and G. Schurz's factual abduction). According to both views, as well (...) as an alternative view, which belongs to Dov M. Gabby and John Woods, selective abduction is different from inference to the best explanation. Schurz's factual abduction depends on all three sub-processes of inference, i.e.: colligation, observation, and judgment (c, o, and j); But Pierce's selective abduction, on the other hand, has only a third sub-process, i.e. judgment process (j), and therefore it cannot be regarded as a process of abductive epistemic inference. (shrink)

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. (shrink)

Traditionally, philosophers have focused mostly on the logical template of inference. The paradigm-case has been deductive inference, which is topic-neutral and context-insensitive. The study of deductive rules has engendered the search for the Holy Grail: syntactic and topic-neutral accounts of all prima facie reasonable inferential rules. The search has hoped to find rules that are transparent and algorithmic, and whose following will just be a matter of grasping their logical form. Part of the search for the Holy Grail (...) has been to show that the so-called scientific method can be formalised in a topic-neutral way. We are all familiar with Carnap’s inductive logic, or Popper’s deductivism or the Bayesian account of scientific method. (shrink)

This paper considers how we decide whether to believe what we are told. Inference to the Best Explanation, a popular general account of non-demonstrative reasoning, is applied to this task. The core idea of this application is that we believe what we are told when the truth of what we are told would figure in the best explanation of the fact that we were told it. We believe the fact uttered when it is part of the (...) class='Hi'>best explanation of the fact of utterance. Having provided some articulation of this account of testimonial inference, the paper goes on to consider whether the account is informative and whether it is plausible. (shrink)

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. (shrink)

An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a (...) working hypothesis in an agent’s further investigations. Along the way, I criticize what I consider to be an overly ambitious conception of the heuristic role of IBE, according to which IBE serves as a guide to absolute probability values. My own conception, by contrast, requires only that IBE can function as a guide to the comparative probability values of available hypotheses. This is shown to be a much more realistic role for IBE given the nature and limitations of the explanatory considerations with which IBE operates. (shrink)

According to C. S. Peirce, abduction is a rational attempt to locate an explanation for a puzzling phenomenon, where this is a process that includes both generating explanatory hypotheses and selecting certain hypotheses for further scrutiny. Since inference is a controlled process that can be subjected to normative standards, essential to his view of abductive rasoning is that it is correlated to a unique species of correctness that cannot be reduced to deductive validity or inductive strength. This irreducibility claim (...) is of considerable importance for the logical and epistemological scrutiny of scientific methods, but it is not clear that Peirce produced a convicing argument for it. To the contrary, when the full structure of abductive argumentation is clarified, especially as presented in Peirce’s later writings on the topic, a good case can be made for viewing every inferential step in the abductive process as dissolving into familiar forms of deductive and inductive reasoning. Specifically, hypothesis-selection is a special type of practical inference that, if correct, is deductively valid, while the creative phase, hypothesis-generation, is not inferential at all. Once this is understood, it emerges that the familiar descriptions of abduction as “inference to the best explanation” or as a process of “belief revision” are, at best, misleading. (shrink)

Inference to the Best Explanation (IBE) advises reasoners to infer exactly one explanation. This uniqueness claim apparently binds us when it comes to “conjunctive explanations,” distinct explanations that are nonetheless explanatorily better together than apart. To confront this worry, explanationists qualify their statement of IBE, stipulating that this inference form only adjudicates between competing hypotheses. However, a closer look into the nature of competition reveals problems for this qualified account. Given the most common explication of competition, this (...) qualification artificially and radically constrains IBE’s domain of applicability. Using a more subtle, recent explication of competition, this qualification no longer provides a compelling treatment of conjunctive explanations. In light of these results, I suggest a different strategy for accommodating conjunctive explanations. Instead of modifying the form of IBE, I suggest a new way of thinking about the structure of IBE’s lot of considered hypotheses. (shrink)

Although the inferring of explanations plays an important role in both our everyday lives and in the workings of science, I argue that inference to the best explanation as it is commonly conceived is often not the best way to capture this sort of reasoning. I suggest that a different form of reasoning – so-called immediate explanatory inference – is instead often much better suited to this task. This is a form of inference in which (...) we are justified in believing explanations for the evidence before us purely in virtue of this evidence, and not in virtue of the evidence plus some general principle or rule of non-deductive reasoning. I defend the idea of such a notion of inference, and argue that it plays a central role in both ordinary life and science. (shrink)

In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation that cannot be accommodated within their framework. In this paper, I survey their worries and attempt to answer them using a series of pragmatic and purely epistemic arguments that I take to show that Bayes’ Rule really is the only rational way to respond to your (...) evidence. (shrink)

An interesting consequence of the structuralist theory of truth approximation, as developed in my From Instrumentalism to Constructive Realism, henceforth ICR, concerns so-called ‘inference to the best explanation’. It will be argued that this popular rule among scientific realists can better be replaced by, various kinds of, ‘inference to the best theory’.

This article considers the prospects of inference to the best explanation as a method of confirming causal claims vis-à-vis the medical evidence of mechanisms. I show that IBE is actually descriptive of how scientists reason when choosing among hypotheses, that it is amenable to the balance/weight distinction, a pivotal pair of concepts in the philosophy of evidence, and that it can do justice to interesting features of the interplay between mechanistic and population level assessments.

I respond to the bad lot argument in the context of biological systematics. The response relies on the historical nature of biological systematics and on the availability of pattern explanations. The basic assumption of common descent enables systematic methodology to naturally generate candidate explanatory hypotheses. However, systematists face a related challenge in the issue of character analysis. Character analysis is the central problem for contemporary systematics, yet the general problem of which it is a case—what counts as evidence?—has not been (...) adequately discussed by proponents of inference to the best explanation. Facing this problem is the price of adopting abductive methods. I sketch an account of how systematists approach the problem of evidence. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from (...) developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

Many philosophers argue that explanatoriness plays no special role in confirmation – that “inference to the best explanation” (IBE) incorrectly demands giving hypotheses extra credit for their potential explanatory qualities beyond the credit they already deserve for their predictive successes. This paper argues against one common strategy for responding to this thought – that is, for trying to fit IBE within a Bayesian framework. That strategy argues that a hypothesis’ explanatory quality (its “loveliness”) contributes either to its prior (...) probability or to its likelihood. This paper argues that this strategy fails because it must give different treatments to two hypotheses that are unlovely for the very same reason. The strategy therefore loses the insight into scientific reasoning that its reconstruction in terms of IBE is supposed to provide. The paper then provides a Bayesian account of the confirmatory role of explanatoriness that represents explanatory quality as having an impact in the same place for two hypotheses that are unlovely for the very same reason. This approach works by “putting explanation back into IBE” – that is, by invoking hypotheses that refer explicitly to scientific explanation and by invoking the agent's background opinions regarding the kind of explanation that the evidence is liable to have. On this approach, there is no list of “explanatory virtues” the possession of which always helps to make an explanation better. Rather, for different facts, there are different characteristics that our background knowledge of other explanations gives us some reason to expect the given fact's explanation to possess. (shrink)

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing (...) is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient''s findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient''s characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems. (shrink)

Christian apologists, like Willian Lane Craig and Stephen T. Davis, argue that belief in Jesus’ resurrection is reasonable because it provides the best explanation of the available evidence. In this article, I refute that thesis. To do so, I lay out how the logic of inference to the best explanation (IBE) operates, including what good explanations must be and do by definition, and then apply IBE to the issue at hand. Multiple explanations—including (what I will call) The (...) Resurrection Hypothesis, The Lie Hypothesis, The Coma Hypothesis, The Imposter Hypothesis, and The Legend Hypothesis—will be considered. While I will not attempt to rank them all from worst to best, what I will reveal is how and why The Legend Hypothesis is unquestionably the best explanation, and The Resurrection Hypothesis is undeniably the worst. Consequently, not only is Craig and Davis’ conclusion mistaken, but belief in the literal resurrection of Jesus is irrational. In presenting this argument, I do not take myself to be breaking new ground; Robert Cavin and Carlos Colombetti have already presented a Bayesian refutation of Craig and Davis’ arguments. But I do take myself to be presenting an argument that the average person (and philosopher) can follow. It is my goal for the average person (and philosopher) to be able to clearly understand how and why the hypothesis “God supernaturally raised Jesus from the dead” fails utterly as an explanation of the evidence that Christian apologist cite for Jesus’ resurrection. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from (...) developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

Aliseda’s Abductive Reasoning is focused on the logical problem of abduction. My paper, in contrast, deals with the epistemic problems raised by this sort of inference. I analyze the relation between abduction and inference to the best explanation (IBE). Firstly a heuristic and a normative interpretation of IBE are distinguished. The epistemic problem is particularly pressing for the latter interpretation, since it is devoid of content without specific epistemic criteria for separating acceptable explanations from those which are (...) not. Then I discuss two different normative interpretations of IBE. I. Niiniliuoto favours a “probabilistic-confirmational” translation of explanatory merit while S. Psillos thinks that the insight of IBE is lost in a pure probabilistic format. My conclusion is that Aliseda’s theory of abduction fits better with a heuristic account of IBE. (shrink)

The uses and interpretation of reductio ad absurdum argumentation in mathematical proof and discovery are examined, illustrated with elementary and progressively sophisticated examples, and explained. Against Arthur Schopenhauer’s objections, reductio reasoning is defended as a method of uncovering new mathematical truths, and not merely of confirming independently grasped mathematical intuitions. The application of reductio argument is contrasted with purely mechanical brute algorithmic inferences as an art requiring skill and intelligent intervention in the choice of hypotheses and attribution (...) of contradictions deduced to a particular assumption in a contradiction’s derivation base within a reductio proof structure. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be make on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the evidence deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is nondemonstrative or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

This article claims the validity of abductive reasoning, or inference to the best explanation, as a practice of discovery of explanatory scientific hypotheses. Along the way to achieve this objective I present here a series of arguments that question the feasibility of Bayesianism as a theory of scientific confirmation. Having solved this issue, I resort to an episode of contemporary astrocosmology that I interpret as an eloquent example of the effectiveness of abductive methodology in contemporary theoretical physics.

What good is logic? -- Seventeen ways this book is different -- The two logics -- All of logic in two pages : an overview -- The three acts of the mind -- I. The first act of the mind : understanding -- Understanding : the thing that distinguishes man from both beast and computer -- Concepts, terms and words -- The problem of universals -- The comprehension and extension of terms -- II. Terms -- Classifying terms -- Categories -- (...) Predicables -- Division -- III. Material fallacies -- Fallacies of language -- Fallacies of diversion -- Fallacies of oversimplification -- Fallacies of argumentation -- Inductive fallacies -- Procedural fallacies -- Metaphysical fallacies -- Short story : love is a fallacy -- IV. Definition -- The nature of definition -- The rules of definition -- The kinds of definition -- The limits of definition -- V. Second act of the mind : judgment -- Judgments, propositions, and sentences -- What is truth? -- The four kinds of categorical propositions -- Logical form -- Euler's circles -- Tricky propositions -- The distribution of terms -- VI. Changing propositions -- Immediate inference -- Conversion -- Obversion -- Contraposition -- VII. Contradiction -- What is contradiction? -- The square of opposition -- Existential import -- Tricky propositions on the square -- Some practical uses of the square of opposition -- VIII. The third act of the mind : reasoning -- What does reason mean? -- The ultimate foundations of the syllogism -- How to detect arguments -- Arguments vs. explanations -- Truth and validity -- IX. Different kinds of arguments -- Three meanings of because -- The four causes -- A classification of arguments -- Simple argument maps -- Deductive and inductive arguments -- Combining deduction and induction : socratic method -- X. Syllogisms -- The structure and strategy of the syllogism -- The skeptics objection to the syllogism -- The empiricist's objection to the syllogism -- Demonstrative syllogisms -- How to construct convincing syllogisms -- XI. Checking syllogisms for validity -- By Euler's circles -- By Aristotle's six rules -- Barbara Celarent : mood and figure -- Venn diagrams -- XII. More difficult syllogisms -- Enthymemes : abbreviated syllogisms -- Sorites : chain syllogisms -- Epicheiremas : multiple syllogisms -- Complex argument maps -- XIII. Compound syllogisms -- Hypothetical syllogisms -- Reductio ad absurdum arguments -- The practical syllogism : arguing about means and ends -- Disjunctive syllogisms -- Conjunctive syllogisms -- Dilemmas -- XIV. Induction -- What is induction? -- Generalization -- Causal induction : Mill's methods -- Scientific hypotheses -- Statistical probability -- Arguments from analogy -- A fortiori and a minore arguments -- XV. Some practical applications of logic -- How to write a logical essay -- How to write a socratic dialogue -- How to have a socratic debate -- How to use socratic method on difficult people -- How to read a book socratically -- XVI. Some philosophical applications of logic -- Logic and theology -- Logic and metaphysics -- Logic and cosmology -- Logic and philosophical anthropology -- Logic and epistemology -- Logic and ethics. (shrink)

Logical abductivism is the epistemic view about logic according to which logical theories are justified by abduction (or Inference to the Best Explanation), that is on how well they explain the relevant evidence, so that the correct logical theory turns out to be the one that explains it best. Arguably, this view should be equally applied to both deductive and non-deductive logics, abduction included. But while there seems to be nothing wrong in principle in using abduction to (...) determine the correct logical theories of deduction and induction, things might be more complicated regarding logical theories of abduction. We may wonder whether allowing for an abductive justification of a theory of abduction is an epistemically legitimate move, since here circularity casts its shadow and makes the situation darker. This is the issue to which this work is devoted. I will defend that, to be effective, an abductive justification for a theory of abduction calls for a justification of abduction in advance, which we do not yet have. (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)

In this book, I will discuss three main topics: the roots and aims of scientific knowledge, scientific knowledge in society, and science and values I understand scientific knowledge as being a planned and continuous production of the general and common knowledge of scientific communities. I begin my discussion with a brief analysis of the main differences between sciences, on the one hand, and everyday experience, philosophies, religions, and ideologies, on the other. I define the concept of science as a set (...) of family resemblances (in Wittgenstein's sense). The sciences can be distinguished along three lines, in terms of the difference between empirical and non-empirical sciences, differences in degrees of theoretical and methodological structure among individual sciences and scientific disciplines. Scientific knowledge is based upon some grounds, paradigmatic knowledge bases. Scientific knowledge is founded on a unified paradigmatic knowledge base. The latter includes fundamental theories, basic facts, generally accepted empirical regularities (laws), scientific research methods, scientific explanations, and established relations to other sciences. Scientific knowledge in general is defined well in classical terms by the definition that knowledge is a true and rationally-justified belief. In this connection, scientific truth is relative, depending on the given paradigmatic knowledge base and on the precision and accessibility of the measurements and observations involved. In the book's first part, I consider the production and testing of scientific hypotheses, scientific explanations, the structure of scientific theories, and the problem of scientific realism. I consider explanation an integral part of a wider nexus of heuristic and theoretical activities in science like giving reasons, understanding, clarification, and founding. Continuous production of scientific explanations is a necessary condition for the systematic reproduction of scientific knowledge. Accordingly, it does not suffice to describe them in terms of the logical structure of the arguments on which they are based, but rather, scientific explanations ought to be given complete with an epistemic and pragmatic context of the explanation. I consider especially deductive-nomological, inductive-statistical, dispositional, teleological-functional, and genetic explanations. I emphasize the ever-increasing importance of explanations by analogy and by model. Orthogonal to these distinctions lies the distinction between causal and epistemic explanation. Epistemic explanations relate scientific discoveries to epistemic progress, and causal explanations are these epistemic explanations that relate scientific discoveries to reality too. Causal explanations inform us that certain changes occur in the world as agents of other changes, or rather, that certain changes are real consequences of other changes. Thus, by having explanations of causes, we can intervene in the world to induce by design certain changes in some course of phenomena. I consider scientific theories as logically and epistemically ordered wholeness of scientific knowledge in a realm of research that enables us to construct useful models of reality. After the discussion of different 'statement' views on theories and different interpretations of the theory-experience relation, I introduce the 'model' theoretical (non-statement) views on theories, especially the structural theory of science. The model views on theories transcend the realism-antirealism dualism by introducing models between theory and reality. Therefore, the question of scientific realism is not merely whether and, if so, which element(s), of scientific theories represent reality, but rather which conditions suffice to infer from a theory that one of its models truly expresses reality. In other words, under which conditions do models of a theory represent models of reality? Generally, models are just as much images of reality in a theory as they are images of a theory in reality. The actual problem of scientific realism doesn't originate in the observable-unobservable distinction (even if this distinction is otherwise theoretically loaded), but rather in ontological quandaries, of the sort especially encountered in modern physical theories such as quantum mechanics, relativity theory, and cosmological theories, although they are known to arise in other sciences as well. Scientific theories may be real, even if they describe nothing about the world. However, such theories can be given this status only if their formal, mathematical postulates exhibit some formal homology between reality and mathematical forms. The very fact that it is possible to describe reality with the help of mathematics even in cases, where every (representational, conceptual) language and thought fall short, would indicate that even mathematics itself is, in some sense, a part of reality, although 'outside' of the empirical world. It follows we have to distinguish the non-empirical reality and the empirical world. Formal objectivity and non-arbitrariness of theories do not emerge from either our language or thoughts, or from the world of facts, causes, and effects, but rather from non-empirical reality, which frames the world as some kind of objective transcendental condition for the possibility of the existence of the world, thoughts, and language. In the second part of the book, I consider the incorporation of scientific work and scientific knowledge into modern society and its relations to values and ethics. Production of scientific knowledge goes on as a parallel distributed process of a special kind of social (shared) cognition. The central part of this cognition is the presentation of parts of the world by scientific (theoretical) models which are warranted by the shared and public practice of justification, and in reflective acting in the world as it would. I consider various formal concepts of collective propositional knowledge (distributed, mutual, common knowledge). Scientific knowledge is an achievement to be ascribed to a scientific community or, indirectly, to all those who apply or expand it, rather than to individuals per se. It presents the continuous and partially planned production of non-trivial common knowledge of the research communities in modern societies. However, besides common knowledge, shared dispositional knowledge (knowledge how of scientific teams) and shared knowledge by acquiring (knowledge of fundamental similarities and differences in scientific teams) are parts of the real body of scientific knowledge. Scientific common knowledge implies the projection into idealized universal knowledge of all real or potentially rational and well-informed individuals. The respect of scientific knowledge thus presents the norm of rationality. Science as the intensively shared and socially distributed kind of cognition enters into modern society as a special kind of work (general work), as a productive force (social invention drive), and as a form of capital (human, social, cultural, informational, etc.). However any of these categories can adequately represent the socio-economical role of science in modern societies, and we are still waiting for a more adequate general concept that would theoretically cover the 'scientification' of modern societies. My thesis is that the most important general effect of scientification is (and will be) the growth of social processes that structurally resemble parallel distributed shared social cognition of science, and not so much the rise of the use of scientific knowledge and scientifically based information in all kinds of production. Scientification of modern societies inevitably includes a much stronger need for moral and social responsibility of scientists for their work and its effects in their local society and the global world. This need comes in contradiction with the quite large accepted idea (and even the 'norm') on value-free research. I discuss to some degree different arguments for the absence of non-epistemic values and ethical reasoning from science and find them non-convincing. The sound idea of value-free research lies in the need for the absence of categorical value statements from scientific explanations and theoretical deductions. Values and ethical judgment have, and sometimes they even have to play an important role in another part of research and its application in science or outside it. Not the value-loudness of research is a danger for science but its blind, non-reflective use. It leads to the ideologization and politicization of science. A similar danger lies in the purely value-neutrality of research. It leads to the instrumentalization of science to the technical or bureaucratic means for 'any' aims. (shrink)

Charles S. Peirce’s conception of abductive reasoning became a hot topic in the philosophy of science after World War II, when N. R. Hanson suggested that abduction is a logic of discovery, Gilbert Harman argued that all types of inductive reasoning can be reduced to inference to the best explanation, and Howard Smokler suggested that abduction as inverse deduction is an important method of confirmation. Abduction has been a popular theme also in Artificial Intelligence. Illustrations and examples of (...) abduction have been sought in everyday life, detective stories, and many scientific disciplines from astronomy to medicine. Iddo Tavory and Stefan Timmermans have published a book on qualitative... (shrink)

The dispute between nomological realists and anti-realists has been reflected in the formulation of various arguments and counterarguments that reach topics as diverse as modality, induction and the very scientific practice. In this context it is common to take the main realist argument –the nomological argument– for an instance of Inference to the Best Explanation, while Nomological Anti-realism is considered a skeptical alternative concerning natural laws, sustained by independent reasons. This paper aims to review that image of (...) the Nomological Realism vs. Anti-realism debate in light of what we believe is an appropriate distinction between abduction and Inference to the Best Explanation. (shrink)

The relationship between Peircean abduction and the modern notion of Inference to the Best Explanation is a matter of dispute. Some philosophers, such as Harman :88–95, 1965) and Lipton, claim that abduction and IBE are virtually the same. Others, however, hold that they are quite different :503, 1998; Minnameier in Erkenntnis 60:75–105, 2004) and there is no link between them :419–442, 2009). In this paper, I argue that neither of these views is correct. I show that abduction and (...) IBE have important similarities as well as differences. Moreover, by bringing a historical perspective to the study of the relationship between abduction and IBE—a perspective that is lacking in the literature—I show that their differences can be well understood in terms of two historic developments in the history of philosophy of science: first, Reichenbach’s distinction between the context of discovery and the context of justification—and the consequent jettisoning of the context of discovery from philosophy of science—and second, underdetermination of theory by data. (shrink)

Evolutionary psychology is a science in the making, working toward the goal of showing how psychological adaptation underlies much human behavior. The knee-jerk reaction that sociobiology is unscientific because it tells “just-so stories” has become a common charge against evolutionary psychology as well. My main positive thesis is that inference to the best explanation is a proper method for evolutionary analyses, and it supplies a new perspective on the issues raised in Schlinger's (1996) just-so story critique. My main (...) negative thesis is that, like many nonevolutionist critics, Schlinger's objections arise from misunderstandings of the evolutionary approach.Evolutionary psychology has progressed beyond telling just-so stories. It has found a host of ingenious special techniques to test hypotheses about the adaptive significance and proximate mechanisms of behavior. Naturalistic data using the comparative method combined with controlled tests using statistical analyses of data provide good evidence for a variety of hypotheses about behavioral control mechanisms — whether in nonhumans or in humans. For instance, the work of Gangestad and Thornhill on evolved mate preferences and fluctuating asymmetry of body type (FA) is a model of success. As the quantity and quality of evidence increase, we are entitled not just to regard such evolutionary hypotheses as preferable, but also as true. Such studies combine to show that the best explanation of “the psychic unity of humankind” — common patterns across societies, history, and cultures exposed by evolutionists — is the gendered, adapted, evolved species-typical design of the mind. (shrink)

The paper deals with the relation between abduction and inference to the best explanation. A heuristic and a normative interpretation of IBE are distinguished. Besides, two different normative interpretations —those vindicated by I. Niiniluoto and S. Psillos— are discussed. I conclude that, in principle, Aliseda’s theory of abduction fits better with a heuristic account of IBE.

As it is standardly conceived, Inference to the Best Explanation (IBE) is a form of ampliative inference in which one infers a hypothesis because it provides a better potential explanation of one’s evidence than any other available, competing explanatory hypothesis. Bas van Fraassen famously objected to IBE thus formulated that we may have no reason to think that any of the available, competing explanatory hypotheses are true. While revisionary responses to the Bad Lot Objection concede that IBE (...) needs to be reformulated in light of this problem, reactionary responses argue that the Bad Lot Objection is fallacious, incoherent, or misguided. This paper shows that the most influential reactionary responses to the Bad Lot Objection do nothing to undermine the original objection. This strongly suggests that proponents of IBE should focus their efforts on revisionary responses, i.e. on finding a more sophisticated characterization of IBE for which the Bad Lot Objection loses its bite. (shrink)