This thesis offers a naturalist revision of Alain Badiou’s philosophy. This goal is pursued through an encounter of Badiou’s mathematical ontology and theory of truth with contemporary trends in philosophy of mathematics and philosophy of science. I take issue with Badiou’s inability to elucidate the link between the empirical and the ontological, and his residual reliance on a Heideggerian project of fundamental ontology, which undermines his own immanentist principles. I will argue for both a bottom-up naturalisation of Badiou’s philosophical approach (...) to mathematics and a top-down naturalisation. Articulating my particular understanding of what realism and naturalism should commit us to, I propose a creative fusion of Badiou’s attention to metamathematical results with a structural-informational metaphysics, proposing a ‘matherialism’ uniting the more daring speculative insights of the former with the naturalist and empiricist commitments motivating the latter. (shrink)

A potential explanation of a fact is a hypothesis such that, if it were true, it would explain the fact in question. Let’s suppose that we become aware of a fact and some potential explanations thereof. Let’s also suppose that we would like to believe the truth. Given this aim, we can ask two questions. First, is it likely that one of these hypotheses is true? Second, given an affirmative answer to the first question, which one is it likely to (...) be? Inference to the best explanation (IBE) offers answers to both questions. To the first, it says ‘Yes’—assuming that at least one of the hypotheses would, if true, provide a satisfactory explanation of the fact under consideration. To the second, it says that the hypothesis most likely to be true is the one that scores best on the explanatory virtues: conservatism, modesty, simplicity, generality, and predictive power. Many philosophers have argued against IBE’s answer to the first question. I am interested in an objection to its answer to the second. Many philosophers seem to think that it is unsustainable: they seem to think that even if we assume that one of the competing hypotheses is true, we should not think that IBE will help us to identify it. Or, more carefully, if these philosophers are doing what they appear to be doing—namely, offering critiques of IBE that don’t depend on assumptions about the field of competing hypotheses—then their claim is that IBE will not help us to identify the truth. I believe that this is mistaken: the argument for believing it assumes a model of IBE that we have no reason to accept. (shrink)

This article concerns the structure of defeasible arguments like: 'If Bob has red spots, Bob has the measles; Bob has red spots; therefore Bob has the measles.' The issue is whether such arguments have the form of modus ponens or not. Either way there is a problem. If they don't have the form of modus ponens, the common opinion to the contrary taught in leading logic textbooks is wrong. But if they do have the form of modus ponens, doubts are (...) raised about the conventional dogma that all arguments having the form of modus ponens are deductively valid. By carefully examining arguments on both sides of the issue, reasonable doubts are raised about the view that all arguments having a modus ponens form are valid. (shrink)

This study aims to understand scientific inference for the evolutionary procedure of Continental Drift based on abductive inference, which is important for creative inference and scientific discovery during problem solving. We present the following two research problems: (1) we suggest a scientific inference procedure as well as various strategies and a criterion for choosing hypotheses over other competing or previous hypotheses; aspects of this procedure include puzzling observation, abduction, retroduction, updating, deduction, induction, and recycle; and (2) we analyze the “theory (...) of continental drift” discovery, called the Earth science revolution, using our multistage inference procedure. Wegener’s Continental Drift hypothesis had an impact comparable to the revolution caused by Darwin’s theory of evolution in biology. Finally, the suggested inquiry inference model can provide us with a more consistent view of science and promote a deeper understanding of scientific concepts. (shrink)

This article presents a formal theory of robot perception as a form of abduction. The theory pins down the process whereby low‐level sensor data is transformed into a symbolic representation of the external world, drawing together aspects such as incompleteness, top‐down information flow, active perception, attention, and sensor fusion in a unifying framework. In addition, a number of themes are identified that are common to both the engineer concerned with developing a rigorous theory of perception, such as the one on (...) offer here, and the philosopher of mind who is exercised by questions relating to mental representation and intentionality. (shrink)

Non-empirical principles have always been a subject of interest of philoso-phers. Authors from different times and traditions agree that principles such as analogy or simplicity are present in the scientific practice. The disagreement comes out when these authors affirm that these principles have an epistemic function, and when they try to present reasons in order to found this state-ment. The first goal of this paper is to describe these principles and to point out their methodological importance. The second goal is (...) to introduce the discussion between the two most important models of philosophical fundamentation – it means, the logicism and the psychologism. The last goal – the main one – is to defend that the non-empirical principals have, legitimately, an epistemic role in the scientific investigation. (shrink)

In his influential paper, 'Why Was the Logic of Discovery Abandoned?', Laudan contends that there has been no philosophical rationale for a logic of discovery since the emergence of consequentialism in the 19th century. It is the purpose of this paper to show that consequentialism does not involve the rejection of all types of logic of discovery. Laudan goes too far in his interpretation of the historical shift from generativism to consequentialism, and his claim that the context of pursuit belongs (...) to neither discovery nor justification is based on narrow interpretations of the contexts of discovery and justification. As a result, Laudan draws unwarranted conclusions concerning both the early and contemporary defenders of a logic of discovery. A methodological logic of discovery - which involves self-corrective methods of hypothesis generation that promote the long-term goals of science and which require consequential support for justification - is a type of logic of discovery that survives the shift to consequentialism. (shrink)

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

Peirce wrote that Hume’s argument against miracles (which is generally liked by twentieth century philosophers for its antireligious conclusion) "completely misunderstood the true nature of" ’abduction’. This paper argues that if Hume’s argumentative strategy were seriously used in all situations (not just those in which we seek to "banish superstition"), it would deliver a choking epistemological conservatism. It suggests that some morals for contemporary naturalistic philosophy may be drawn from Peirce’s argument against Hume.

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)

In this paper I argue for a place for logic inscientific methodology, at the same level asthat of computational and historicalapproaches. While it is well known that a awhole generation of philosophers dismissedLogical Positivism (not just for the logicthough), there are at least two reasons toreconsider logical approaches in the philosophyof science. On the one hand, the presentsituation in logical research has gone farbeyond the formal developments that deductivelogic reached last century, and new researchincludes the formalization of several othertypes of (...) reasoning, like induction andabduction. On the other hand, we call for abalanced Philosophy of Science, one inwhich both methods, the formal and thehistorical may be complementary, togetherproviding a pluralistic view of science, inwhich no method is the predominant one. (shrink)

The present study has two goals: to explore elementary students’ understanding of evidence and the ways they deploy it to construct arguments, and to examine whether eliciting their concept of evidence during argumentation improves students’ evidence-based reasoning. Individual semi-structured interviews were conducted with 4th and 6th graders in a public school in Mexico. We found significant differences between groups regarding the concept of evidence, with better performance in the older group. A positive correlation between the concept of evidence and the (...) quality of evidence-based reasoning was found. Also, three performance profiles were observed after eliciting the concept of evidence when grade was excluded as a factor. Results suggest that the concept of evidence plays an essential role in developing argumentative competence in pre-adolescence. (shrink)

Taking as its exegetic point of departure Peirce's outline of a semiotic theory of cognition from the mid 1890s, this paper explores the relevance of this outline to a theory of learning and also to a broader, normative vision of education. Firstly, besides providing for fallibilism in philosophical inquiry Peirce's outline accords with critical strategies of his fellow pragmatists, such as William James's detection of the ‘psychologist's fallacy’ and John Dewey's rejection of the ‘philosophical fallacy’. It is pointed out that (...) this merit of Peirce's outline is due to his abstract and often neglected notion of a prespecialised scientific intelligence. Secondly, the paper shows that Peirce's semiotic approach to prescientific learning processes conditioning all kinds of theoretical activity may indeed complement James's and Dewey's contributions: giving careful attention to the work of linguistic and paralinguistic signs in such learning processes Peirce's analysis captures indexical and iconic aspects, as well as symbolic aspects of linguistic interaction and experientially constrained dialogue. Following Peirce's analysis the paper considers how the iconicity of linguistic structure, ostensive acts and the practical situatedness of language use are pre‐conditions of experiential learning in every day life as well as in scientific learning. It is also shown how this semiotic analysis is connected with Peirce's important notion of abduction. Finally, it is pointed out that a broader vision of education may issue from Peirce's semiotic analysis and that such a vision would emphasise that the life experience of individuals entering educational institutions should be made to bear on the objectives as well as the teaching practices of educational institutions. (shrink)

Taking as its exegetic point of departure Peirce's outline of a semiotic theory of cognition from the mid 1890s, this paper explores the relevance of this outline to a theory of learning and also to a broader, normative vision of education. Firstly, besides providing for fallibilism in philosophical inquiry Peirce's outline accords with critical strategies of his fellow pragmatists, such as William James's detection of the ‘psychologist's fallacy’ and John Dewey's rejection of the ‘philosophical fallacy’. It is pointed out that (...) this merit of Peirce's outline is due to his abstract and often neglected notion of a prespecialised scientific intelligence. Secondly, the paper shows that Peirce's semiotic approach to prescientific learning processes conditioning all kinds of theoretical activity may indeed complement James's and Dewey's contributions: giving careful attention to the work of linguistic and paralinguistic signs in such learning processes Peirce's analysis captures indexical and iconic aspects, as well as symbolic aspects of linguistic interaction and experientially constrained dialogue. Following Peirce's analysis the paper considers how the iconicity of linguistic structure, ostensive acts and the practical situatedness of language use are pre‐conditions of experiential learning in every day life as well as in scientific learning. It is also shown how this semiotic analysis is connected with Peirce's important notion of abduction. Finally, it is pointed out that a broader vision of education may issue from Peirce's semiotic analysis and that such a vision would emphasise that the life experience of individuals entering educational institutions should be made to bear on the objectives as well as the teaching practices of educational institutions. (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)

One of the crucial problems of argumentation schemes as illustrated in (Walton, Reed & Macagno 2008) is their practical use for the purpose of analyzing texts and producing arguments. The high number and the lack of a classification criterion make this instrument extremely difficult to apply practically. The purpose of this paper is to analyze the structure of argumentation schemes and outline a possible criterion of classification based on alternative and mutually-exclusive possibilities. Such a criterion is based not on what (...) an argument is, but how it can be understood and interpreted. The schemes are grouped according to an end-means principle, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical (...) connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

This volume examines the limitations of mathematical logic and proposes a new approach to logic intended to overcome them. To this end, the book compares mathematical logic with earlier views of logic, both in the ancient and in the modern age, including those of Plato, Aristotle, Bacon, Descartes, Leibniz, and Kant. From the comparison it is apparent that a basic limitation of mathematical logic is that it narrows down the scope of logic confining it to the study of deduction, without (...) providing tools for discovering anything new. As a result, mathematical logic has had little impact on scientific practice. Therefore, this volume proposes a view of logic according to which logic is intended, first of all, to provide rules of discovery, that is, non-deductive rules for finding hypotheses to solve problems. This is essential if logic is to play any relevant role in mathematics, science and even philosophy. To comply with this view of logic, this volume formulates several rules of discovery, such as induction, analogy, generalization, specialization, metaphor, metonymy, definition, and diagrams. A logic based on such rules is basically a logic of discovery, and involves a new view of the relation of logic to evolution, language, reason, method and knowledge, particularly mathematical knowledge. It also involves a new view of the relation of philosophy to knowledge. This book puts forward such new views, trying to open again many doors that the founding fathers of mathematical logic had closed historically. (shrink)

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity (...) of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

Charles S. Peirce (1839-1914) made relevant contributions to deductive logic, but he was primarily interested in the logic of science, and more especially in what he called 'abduction' (as opposed to deduction and induction), which is the process whereby hypotheses are generated in order to explain the surprising facts. Indeed, Peirce considered abduction to be at the heart not only of scientific research, but of all ordinary human activities. Nevertheless, in spite of Peirce's work and writings in the field of (...) methodology of research, scarce attention has been paid to the logic of discovery over the last hundred years, despite an impressive development not only of scientific research but also of logic. -/- Having this in mind, the exposition is divided into five parts: 1) a brief presentation of Peirce, focusing on his work as a professional scientist; 2) an exposition of the classification of inferences by the young Peirce: deduction, induction and hypothesis; 3) a sketch of the notion of abduction in the mature Peirce; 4) an exposition of the logic of surprise; and finally, by way of conclusion, 5) a discussion of this peculiar ability of guessing understood as a rational instinct. -/- . (shrink)

This volume presents a selection of papers reflecting key theoretical issues in argumentation theory. Its six sections are devoted to specific themes, including the analysis and evaluation of argumentation, argument schemes and the contextual embedding of argumentation. The section on general perspectives on argumentation discusses the trends of empiricalization, contextualization and formalization, offers descriptions of the analytical and evaluative tools of informal logic, and highlights selected principles that argumentation theorists do and do not agree upon. In turn, the section on (...) linguistic approaches to argumentation focuses on the problem of distinguishing between explanation and argument, while also elaborating on the role of verbal indicators of argument schemes. All essays included in this volume point out notable recent developments in the study of argumentation. (shrink)

Enhanced indispensability arguments claim that Scientific Realists are committed to the existence of mathematical entities due to their reliance on Inference to the best explanation. Our central question concerns this purported parity of reasoning: do people who defend the EIA make an appropriate use of the resources of Scientific Realism to achieve platonism? We argue that just because a variety of different inferential strategies can be employed by Scientific Realists does not mean that ontological conclusions concerning which things we should (...) be Scientific Realists about are arrived at by any inferential route which eschews causes, and nor is there any direct pressure for Scientific Realists to change their inferential methods. We suggest that in order to maintain inferential parity with Scientific Realism, proponents of EIA need to give details about how and in what way the presence of mathematical entities directly contribute to explanations. (shrink)

In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.

Explaining phenomena is a primary goal of science. Consequently, it is unsurprising that gaining a proper understanding of the nature of explanation is an important goal of science education. In order to properly understand explanation, however, it is not enough to simply consider theories of the nature of explanation. Properly understanding explanation requires grasping the relation between explanation and understanding, as well as how explanations can lead to scientific knowledge. This article examines the nature of explanation, its relation to understanding, (...) and how explanations are used to generate scientific knowledge via inferences to the best explanation. Studying these features and applications of explanation not only provides insight into a concept that is important for science education in its own right, but also sheds light on an aspect of recent debates concerning the so-called consensus view of nature of science (NOS). Once the relation between explanation, understanding, and knowledge is clear, it becomes apparent that science is unified in important ways. Seeing this unification provides some support for thinking that there are general features of NOS of the sort proposed by the consensus view and that teaching about these general features of NOS should be a goal of science education. (shrink)

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. (shrink)

This research was carried out to explore some of the cognitive processes involved in scientific anomalies resolution. 40 subjects with a good neuropsychology expertise were asked to explain two (invented) anomalous neuropsychological cases. The subjects' efforts to give a meaningful structure to the data were recorded, and the resulting reasoning blocks were analysed to extract and compute the inferential (deductive, inductive and abductive) and analogical processes used. The processes were intercorrelated to experimentally verify the co-occurrence of different forms of logical (...) thinking. Statistical analysis point out the relevance of abductive inferences, the possible presence of an inferential-style switching process , the high number of external analogies used, the cognitive closeness manifested by expert reasoners. (shrink)

Competence to enact responsible practices, such as recycling waste or boycotting irresponsible companies, is core to learning for responsibility. We explore the role of apps in learning such responsible practices ‘in the wild,’ outside formal educational environments over a 3-week period. Learners maintained a daily diary in which they reflected on their learning of responsible practices with apps. Through a thematic analysis of 557 app mentions in the diaries, we identified five types of app-agency: cognitive, action, interpersonal, personal development, and (...) material. Findings were interpreted from an actor-network perspective using the lens of ‘translation.’ To understand how apps enabled the learning of responsible practices, we analyzed app agency throughout four moments of translation: problematization, interessement, enrolment, and mobilization. Based on our analysis of how students’ app mentions changed over time, we further theorize learning as a sequence of subtranslations that form the larger translation process: learning as translation(s). Each subtranslation cycle is centered on enrolling a different set of human and nonhuman actors, with their competence, into the network. We contribute to the learning for responsibility field by showcasing how app-enabled learning may create real-life actor networks enacting responsibility, and by priming an actor-network pedagogy for ‘learning in the wild.’ We also contribute to the actor-network learning discussion by conceptualizing heterogeneous human–nonhuman competence and the first processual model of learning as translation(s). (shrink)

This paper describes an abductive process model of anomalous data integration. The model makes use of the entrenchment of the current explanation and the probability of alternative explanations. It is hypothesised that increasing confirmation of the anom-aly itself increases the probability of alternative explanations. In an experimental study we found that both the entrenchment of an existing explanation and confirmation of the anomaly clearly influence how people resolve anomalous data. These results are in agreement with the predic-tions of the model.

In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2n composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present composite hypothesis of that evidence; however, it is unclear when and how the current explanation is used. In addition, although a person's (...) current explanation can narrow the search for a hypothesis, it can also blind the problem solver to alternative, possibly better, explanations. This paper describes a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation. The first prediction is that the current explanation is not used to explain new evidence if there is a simple (i.e., nondisjunctive, concrete) hypothesis to account for that evidence. The second prediction is that the current explanation is used when attempting to discriminate among several alternative hypotheses for new evidence. These hypotheses were tested in three experiments. The results are consistent with the second prediction: the current explanation is used when discriminating among alternative hypotheses. However, the first prediction—that the current explanation is not used when a simple hypothesis can account for new data—received only limited support. Participants used the current explanation to constrain their interpretation of new data in 46.5% of all trials. This suggests that context‐independent strategies compete with context‐dependent ones—an interpretation that is consistent with recent work on strategy selection during problem solving. (shrink)

In Artificial Intelligence, abduction is often formalized in first-order logic. In this article, we focus on the problem of identifying equivalence of two abductive theories represented in first-order logic. To this end, two definitions of equivalence are given for abduction. Explainable equivalence requires that two abductive theories have the same explainability for any observation. On the other hand, explanatory equivalence guarantees that any observation has exactly the same explanations in each abductive theory. Explanatory equivalence is a stronger notion than explainable (...) equivalence. In first-order logic, explainable equivalence can be veri.ed by the notion of extensional equivalence in default theories, while explanatory equivalence reduces to logical equivalence between background theories. We also show the complexity results for abductive equivalence. (shrink)

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g., theorems in mathematical logic) whose truth was not even known prior to the existence of the explanation (proof). What do the cognitive operations underlying the inference that the milk is sour have in common with the proof that, say, the square root of two (...) is irrational? Our ability to generate explanations bears striking similarities to our ability to make analogies. Both reflect a capacity to generate inferences and generalizations that go beyond the featural similarities between a novel problem and familiar problems in terms of which the novel problem may be understood. However, a notable difference between analogy-making and explanation-generation is that the former is a process in which a single source situation is used to reason about a single target, whereas the latter often requires the reasoner to integrate multiple sources of knowledge. This seemingly small difference poses a challenge to the task of marshaling our understanding of analogical reasoning to understanding explanation. We describe a model of explanation, derived from a model of analogy, adapted to permit systematic violations of this one-to-one mapping constraint. Simulation results demonstrate that the resulting model can generate explanations for novel explananda and that, like the explanations generated by human reasoners, these explanations vary in their coherence. (shrink)

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.

The question of whether or not to partition data for the purposes of inferring phylogenetic hypotheses remains controversial. Opinions have been especially divided since Kluge's (1989, Systematic Zoology 38, 7–25) claim that data partitioning violates the requirement of total evidence (RTE). Unfortunately, advocacy for or against the RTE has not been based on accurate portrayals of the requirement. The RTE is a basic maxim for non-deductive inference, stipulating that evidence must be considered if it has relevance to an inference. Evidence (...) is relevant if it has a positive or negative effect on a given conclusion. In the case of ℈partitioned’ phylogenetic inferences, the RTE is violated, and the basis for rational belief in any conclusion is compromised, unless it is shown that the partitions are evidentially irrelevant to one another. The goal of phylogenetic systematics is to hypothesize past causal conditions to account for observed shared similarities among two or more species. Such inferences are non-deductive, necessitating consideration of the RTE. Some phylogeneticists claim the parsimony criterion as justification for the RTE. There is no relation between the two – parsimony is a relation between a hypothesis and causal question(s). Parsimony does not dictate the content of premises prior to an inference. ℈Taxonomic congruence,’ ℈supertrees,’ and ℈conditional combination’ methods violate the RTE. Taxonomic congruence and supertree methods also fail to achieve the intended goal of phylogenetic inference, such that ℈consensus trees’ and ℈supertrees’ lack an empirical basis. ℈Conditional combination’ is problematic because hypotheses derived from partitioned data cannot be compared – a causal hypothesis inferred to account for a set of effects only has relevance to those effects, not any comparative relevance to other causal hypotheses. A similar problem arises in the comparisons of hypotheses derived from different causal theories. (shrink)

Three competing ‘methods’ have been endorsed for inferring phylogenetic hypotheses: parsimony, likelihood, and Bayesianism. The latter two have been claimed superior because they take into account rates of sequence substitution. Can rates of substitution be justified on its own accord in inferences of explanatory hypotheses? Answering this question requires addressing four issues: (1) the aim of scientific inquiry, (2) the nature of why-questions, (3) explanatory hypotheses as answers to why-questions, and (4) acknowledging that neither parsimony, likelihood, nor Bayesianism are inferential (...) actions leading to explanatory hypotheses. The aim of scientific inquiry is to acquire causal understanding of effects. Observation statements of organismal characters lead to implicit or explicit why-questions. Those questions, conveyed in data matrices, assume the truth of observation statements, which is contrary to subsequently invoking substitution rates within inferences to phylogenetic hypotheses. Inferences of explanatory hypotheses are abductive in form, such that some version of an evolutionary theory(ies) is/are included or implied. If rates of sequence evolution are to be considered, it must be done prior to, rather than within abduction, which requires renaming those putatively-shared nucleotides subject to substitution rates. There are, however, no epistemic grounds for renaming characters to accommodate rates, calling into question the legitimacy of causally accounting for sequence data. (shrink)

The aim of this paper is to explore the conditions under which generalisation from single case studies, in the sense of making inferences concerning a wider class of phenomena beyond a case, is reasonable. Two sets of conditions, in particular, provide the basis for our consideration of this issue. The first is an exploration of the impressive amount of empirical knowledge that is contained within the theories that are used to make observations, to classify phenomena, and to understand and interpret (...) cases. The second is recognition of the role of a pattern of inference known as abduction, or inference to the best explanation, in drawing conclusions from case studies. It is argued that these two conditions, when applied to the procedures for conducting case studies, can be used to defend, as reasonable, the making of certain provisional generalisations from single cases. An epistemological procedure for testing and further justifying these generalisations, with a particular emphasis on dealing with the problem of confirmation bias, is then discussed and recommended. (shrink)

The 20th century philosophy of science began on a positivistic note. Its focal point was scientific explanation and the hypothetico-deductive (HD) framework of explanation was proposed as the standard of what is meant by “science.” HD framework, its inductive and statistical variants, and other logic-based approaches to modeling scientific explanation were developed long before the dawn of the information age. Since that time, the volume of observational data and power of high performance computing have increased by several orders of magnitude (...) and reshaped the practice and concept of science, and indeed, the philosophy of science. A new observational-inductive (OI) framework for scientific research is emerging due to recent developments in sensors, data systems, computers, and knowledge discovery techniques. We examine the nature of these changes and their impact on the question of what is meant by “science” after discussing five examples of the OI framework, and conclude that the HD and OI frameworks are complementary and synergistic. (shrink)

Collaborative learning with cases characteristically involves discussing and developing shared explanations. We investigated the argumentation scheme which learners use in constructing shared explanations over evidence. We observed medical students attempting to explain how a judge had arrived at his verdict in a case of medical negligence. The students were learning within a virtual learning environment and their communication was computer mediated. We identify the dialogue type that these learners construct and show that their argumentation conforms with an abductive form of (...) argumentation scheme (‘inference to the best explanation’). We also assessed the students’ learning and propose that it is related to particular features of this argumentation scheme. (shrink)

There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general (...) idea via recent work on explanationist models of updating, which are fundamentally probabilistic but assign a substantial, non-Bayesian role to explanatory considerations. (shrink)

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

Abduction and metaphor are two significant concepts in cognitive science. It is found that the both mental processes are on the basis of certain similarity. The similarity inspires us to seek the answers to the following two questions: Whether there is a common cognitive mechanism behind abduction and metaphor? And if there is, whether this common mechanism could be interpreted within the unified frame of modern intelligence theory? Centering on these two issues, the paper attempts to characterize and interpret the (...) generation and evolution of scientific metaphors from the perspective of the cognitive mechanism of abductive inference. Then it interprets the common cognitive mechanism behind abduction and metaphor within Hawkins’ frame of intelligence theory. The commonality between abduction and metaphor indicates the potential to further explore human intelligence. (shrink)

This paper stems from previous works of the authors in which a new measure of the simplicity of an explanation based on its degree of arbitrariness is proposed: The more the explanation is arbitray, the less appealing it is, with explanations having no arbitrariness – called constrained – being the preferred ones. In previous works, as commonly done in the literature of abductive logic programming, a set of hypotheses is not an explanation, unless it is definite, i.e. it explains all (...) the data belonging to the observation. In this paper, we follow a different perspective and define the concept of indefinite constrained explanations, i.e. constrained explanations that are not definite, but admit some indefiniteness. An indefinite constrained explanation captures the intuition of the existence of an explanation (indefinite explanation) that would best explain the given evidence, while not making arbitrary choices (constrained explanation). The main contribution of the paper is the study of abduction in indefinite deductive theories: specifically, the paper studies the framework of abductive logic programming extended with integrity constraints in the setting in which both the initial knowledge base and the abductive explanations are indefinite (may contain occurrences of null values) and the domain is possibly infinite. Furthermore, the paper discusses the complexity of problems concerning indefinite (constrained) explanations. (shrink)