In this paper we will show Peirce’s distinction between deduction, induction and abduction. The aim of the paper is to show how Peirce changed his views on the subject, from an understanding of deduction, induction and hypotheses as types of reasoning to understanding them as stages of inquiry very tightly connected. In order to get a better understanding of Peirce’s originality on this, we show Peirce’s distinctions between qualitative and quantitative induction and between theorematical and corollarial deduction, passing then (...) to the distinction between mathematics and logic. In the end, we propose a sketch of a comparison between Peirce and Whitehead concerning the two thinkers’ view of mathematics, hoping that this could point to further inquiries. (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)

Automated Software Testing (AST) using Model Checking is in this article epistemologically analysed in order to argue in favour of a model-based reasoning paradigm in computer science. Preliminarily, it is shown how both deductive and inductive reasoning are insufficient to determine whether a given piece of software is correct with respect to specified behavioural properties. Models algorithmically checked in Model Checking to select executions to be observed in Software Testing are acknowledged as analogical models which establish isomorphic relations (...) with the target system’s data set. Analogical models developed in AST are presented as abductive models providing hypothetical explanations to observed executions. The model assumption—algorithmic check—software testing process is understood as the abduction—deduction—induction process defining the selective abduction and turned to isolate a set of model-based hypotheses concerning the target system behaviours. A manipulative abduction process is finally recognized in the practice of adapting, abstracting and refining models that do not provide successful predictions. (shrink)

Essentialism is often criticized for producing biased behavior. Because it is a view through which people attempt to grasp the essence of things, it appears contradictory that essentialism might result in distortions of reality. Somewhere within essentialist cognitive processes there must be mistakes or omissions that fail to capture reality correctly. In this paper, I treat essentialization as an abductive reasoning process, as a hypothesis, that explains particular characteristics of people on the basis of category membership alone. Besides (...) essentialization, essentialist reasoning can also include deductive and inductive processes that aim at elaborating and testing initial hypotheses. Therefore, essentialist beliefs can be built by essentialization and hypotheses alone or they can be the product of more elaborate reasoning. The relationship of essentialization and general essentialist reasoning to truth and knowledge is discussed. (shrink)

Reasoning over our knowledge bases and theories often requires non-deductive inferences, especially – but by no means only – when commonsense reasoning is the case, i.e. when practical agency is called for. This kind of reasoning can be adequately formalized via the notion of supraclassical consequence, a non-deductive consequence tightly associated with default and non-monotonic reasoning and featuring centrally in abductive, inductive, and probabilistic logical systems. In this paper, we analyze core concepts and problems of (...) these systems in the light of supraclassical consequence. (shrink)

Abduction, deduction and induction are different forms of inference in science. However, only a few attempts have been made to introduce the idea of abductive reasoning as an extended way of thinking about clinical practice in nursing research. The aim of this paper was to encourage critical reflections about abductive reasoning based on three empirical examples from nursing research and includes three research questions on what abductive reasoning is, how the process has taken place, (...) and how knowledge about abductive reasoning based on the examples can inform nursing research and clinical practice. The study has a descriptive and explorative approach using a convenience sample of three empirical studies from nursing research. The three studies illustrate different ways to enter the abductive reasoning process in steps. They represent new caring models, which offer visual and cognitive maps for expanding nursing research, education and thus informing care. Therefore, we suggest that abductive reasoning may be beneficial for different ways of knowing and demonstrates scientific innovation to shed new light on health phenomena, which can help researchers and practitioners to gain a broader and deeper understanding of nursing care inquiry. However, more studies are needed to broaden this scope. (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)

In this paper, we set out to investigate the following question: if science relies heavily on induction, does philosophy of science rely heavily on induction as well? Using data mining and text analysis methods, we study a large corpus of philosophical texts mined from the JSTOR database (n = 14,199) in order to answer this question empirically. If philosophy of science relies heavily on induction, just as science supposedly does, then we would expect to find significantly more inductive arguments than (...) deductive arguments and abductive arguments in the published works of philosophers of science. Using indicator words to classify arguments by type (namely, deductive, inductive, and abductive arguments), we search through our corpus to find patterns of argumentation. Overall, the results of our study suggest that philosophers of science do rely on inductive inference. But induction may not be as foundational to philosophy of science as it is thought to be for science, given that philosophers of science make significantly more deductive arguments than inductive arguments. Interestingly, our results also suggest that philosophers of science do not rely on abductive arguments all that much, even though philosophers of science consider abduction to be a cornerstone of scientific methodology. (shrink)

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.

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)

Debates concerning the character, scope, and warrant of abductive inference have been active since Peirce first proposed that there was a third form of inference, distinct from induction and deduction. Abductive reasoning has been dubbed weak, incoherent, and even nonexistent. Part, at least, of the problem of articulating a clear sense of abductive inference is due to difficulty in interpreting Peirce. Part of the fault must lie with his critics, however. While this article will argue that (...) Peirce indeed left a number of puzzles for interpreters, it will also contend that interpreters should be careful to distinguish discussion of the formal and strictly epistemic question of whether and how abduction is a sound form of inference from discussions of the practical goals of abduction, as Peirce understood them. This article will trace a history of critics and defenders of Peirce’s notion of abduction and discuss how Peirce both fueled the confusion and in fact anticipated and responded to several recurring objections. (shrink)

Essential to Peirce's distinction among three kinds of reasoning, deduction, induction and abduction, is the claim that each is correlated to a unique species of validity irreducible to that of the others. In particular, abductive validity cannot be analyzed in either deductive or inductive terms, a consequence of considerable importance for the logical and epistemological scrutiny of scientific methods. But when the full structure of abductive argumentation — as viewed by the mature Peirce — is clarified, every (...) inferential step in the process can be seen to dissolve into familiar forms of deductive and inductive reasoning. Specifically, the final stage is a special type of practical inference which, if correct, is deductively valid, while the creative phase, surprisingly, is not inferential at all. In neither is abduction a type of inference to the best explanation. The result is a major reassessment of the relevance of Peirce's views to contemporary methodological studies. (shrink)

How do you tell what’s right from what’s wrong? Can you always? What’s the difference between deduction, induction, and abduction? What are the best techniques for making an argument logically sound? In this fascinating little book, the smallest on its subject ever produced, philosopher Earl Fontainelle explores the ancient art of discursive Logic and demonstrates some of the techniques that have long been used to triumph over the debates and deceptions that assail us every day. Filled with helpful examples of (...) good and bad reasoning, Logic is an invaluable introduction to a defining human characteristic. (shrink)

Lesson observation is frequently used in teacher induction programmes to support newly-qualified teachers in their reflection and classroom enquiry. This article uses an elaboration of Peirce’s abduction to illustrate how the post-observation conversation supports a teacher’s reflection on her teaching, and in particular, her teaching of language to young children. It shows that abduction involves an expert-like intuition, where the interaction and co-enquiry with the advisor was crucial. The analytical framework used is based on six modes of abductive (...) class='Hi'>reasoning or inference that deal with potential or possibility, three modes of induction dealing with actuality, and one mode of deduction focusing on rules and regulations. In a context of lifelong learning, with emphasis on teacher learning across the professional lifespan, the article shows how newly-qualified teachers can be supported in using abductive reasoning and engage in worthwhile classroom enquiry. The article contributes to edusemiotics, a recently developed direction in educational theory that explores the philosophical specifics of semiotics in educational contexts. (shrink)

The plausible reasoning class (called the JSM-reasoning in honour of John Stuart Mill) is described. It implements interaction of three forms of non-deductive procedures  induction, analogy and abduction. Empirical induction in the JSM-reasoning is the basis for generation of hypotheses on causal relations (determinants of social behaviour). Inference by analogy means that predictions about previously unknown properties of objects (individual’s behaviour) are inferred from causal relations. Abductive inference is performed to check on the explanatory adequacy (...) of generated hypotheses. To recognize rationality of respondents’ opinion deductive inference is used. Plausible reasoning, semantics of argumentation logic and deductive recognition of opinion rationality represent logical tool for cognitive sociology problems. (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)

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)

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 paper practical reasoning is understood in the Aristotelian sense as reasoning leading to action or to an intention to do something. Georg Henrik von Wright and a number of other philosophers have tried to assimilate certain forms of such reasoning to deductive reasoning. Many examples of practical reasoning, including some examples given by Aristotle, do not fit a deductive or quasi‐deductive model. It is argued that instances of good practical reasoning often resemble (...) abductive rather than deductive or inductive reasoning, and that the principles governing abduction, including the Principle of Economy, are applicable to practical reasoning. (shrink)

I begin with a typology of reasoning and cross it with types of processes. I demonstrate that the thrust of Plato’s Republic is theory-building. This involves the critical and dialectic processes which are paradigms of Platonic methodology. Book I displays abductive analogical reasoning joined by an induction that is embedded in a deduction; hence there is a deduction–induction–abduction chain. In Book VI, Plato constructs a visual model of the divided line, which also displays model-based and abductive (...) hypothesis generation that is essential to theory building. Book VII provides an abductive metaphor model of the allegory of the cave. Both models depict degrees of reality and the ascendency of knowledge. The multimodal model-based allegory has far reaching applications from criminal justice to information systems. I conclude by capturing the narrative of the Republic as a critical and dialectic process of theory building using deductive–inductive–abductive chains, an abductive visual model and an abductive metaphor model. Hence, the Republic is simultaneously a masterpiece of deductive reasoning and a marvel of complex model-based abduction, involving visual models, analogies and metaphors. (shrink)

Abduction, deduction and induction describe forms of reasoning. Deduction and induction are discussed in the nursing literature. However, abduction has been largely neglected by nurse scholars. In this paper it is proposed that abduction may play a part in qualitative data analysis – specifically, in the identification of themes, codes, and categories. Abduction is not, in research, restricted to or associated with any particular methodology. Nevertheless, situating abduction in qualitative research facilitates the identification of three interlinked issues. First, it (...) is suggested that abductively derived claims require support from deductive and inductively sourced evidence if they are to ‘hold’ and, yet, in qualitative research this is clearly problematic. Second, difficulties in choosing between alternative plausible hypotheses are explored through an examination of the ‘generality problem’. Third, the role of background and auxiliary theories in adjudicating between hypothesis options is discussed. It is argued that if qualitative researchers utilize abductive inference in the manner suggested, then the peculiarly fallible nature of abduction must be acknowledged and, in consequence, the action guiding potential of qualitative research findings is compromised. (shrink)

Charles Sanders Peirce wrote the article “The probability of induction” in 1878. It was the fourth article of the series “Illustrations of the Logic of Science” which comprised a total of six articles. According to Peirce, to get a clear idea of the conception of probability, one has ‘to consider what real and sensible difference there is between one degree of probability and another.’ He endorsed what John Venn had called the ‘materialistic view’ of the subject, namely that probability is (...) the proportion of times in which an occurrence of one kind is accompanied by an occurrence of another kind. On the other hand, Peirce recognized the existence of a different interpretation of probability, which was termed by Venn the ‘conceptualistic view’, namely the degree of belief that ought to be attached to a proposition. Peirce’s intent on writing this article seems to be to inquire about the claims of the conceptualists concerning the problem of induction. After reasoning on some examples, he concluded on the impossibility of assigning probability for induction. We show here that the arguments advanced in his article are not sufficient to support such conclusion. Peirce’s thoughts on the probability of induction surely may have influenced statisticians and research scientists of the 20th century in shaping data analysis. (shrink)

[English in PhilArchive, unpublished]. Ruđer Bošković's (Rogerius Joseph Boscovich, 1711-1787) induction is described as a reasoning procedure that combines abductive, generalizing and deductive forms of inference. According to Bošković, the application of inductive reasoning extends beyond natural science. Bošković's critique of the use of the principle of sufficient reason is discussed, and constructive rules of Bošković's inductive logic are proposed from the standpoint of contemporary justification logic. To that end, justification logic could be extended with Bošković's typology (...) of reasons. Hunter's result (1965) is conceived as a confirmation that the inference from the constructive defectus rationis is a necessary component of Bošković's induction. - English version in _PhilArchive_. (shrink)

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)

Peirce’s claims that methodeutic “concerns abduction alone” and that “pragmatism contributes to the security of reasoning but hardly to its uberty” are explained. They match as soon as a third claim is taken into account, namely that “pragmatism is the logic of abduction,” not of deduction or induction. Since methodeutic concerns abduction and not deduction or induction, it follows that pragmatism is a maxim of methodeutic. Then, since pragmatism contributes to the security of reasoning but not to its (...) uberty, it follows that methodeutic contributes to the security of the only reasoning it is concerned with, namely abduction. We then explain two related issues of methodeutic of abduction. First, in addition to the maxim of pragmatism, which suggests how to choose among experimental hypotheses contributing to the security of reasoning, there is the maxim of simplicity, which suggests hypotheses that are preferable for investment and which contributes to uberty of reasoning. Second, a third maxim of abduction is economy, which suggests adopting hypotheses that contribute to the advantageousness of reasoning even when pragmatism and simplicity cease to apply. These three maxims—experientiality for security, simplicity for uberty, and economy for advantageousness—are the bedrocks of Peirce’s methodeutic of abduction. (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)

The purpose of this study was to investigate the features of modeling-based abductive reasoning as a disciplinary practice of inquiry in the domain of earth science. The study was based on an undergraduate course of a university of education, Korea, offered for preservice elementary teachers majoring in science as their specialty. The course enrollees participated in an inquiry project in which they were asked to abductively generate models representing past geologic events in order to explain how two units (...) in a sedimentary rock outcrop had been formed. Three students were selected as major informants for the study, and multiple types of qualitative data were collected, including the students’ sketchbook records, audio-recording of whole-class presentation and discussion, and interviews with the students. The data were analyzed according to the method of analytical induction, which yielded three assertions as the findings of the study. First, while an explanatory model can be generated by combining resource models, the combination of resource models does not necessarily result in a scientifically sound explanatory model. Second, a systemic approach can help activate a critical resource model which can in turn lead to a scientifically sound explanatory model. Third, simulations with a model can enhance the plausibility of the model. Based on these findings, causal combinations of resource models, a systemic approach to earth scientific problem-solving, and simulations with a model are suggested and discussed as implications for science education and relevant research. (shrink)

This is an analysis of Peirce's theory of the three types of reasoning as explained in his 1877 essay "Deduction, Induction, and Hypothesis", which is representative of his earlier views on this subject. Hypothesis -which l-ter would become his abduction or retroduction- and induction are considered variants of probable syllogisms of the second and third figures. Specific attention is devoted to similarities and differences between induction and hypothesis.

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)

Argumentation or reasoning is the act of rendering theses rationally justified or plausible, through appeal to specific premises and the use of logical inferences. The principal forms of logical inference are deductive, inductive, and abductive. The chapter outlines some common argumentative fallacies and discusses important modes of argumentation, such as the use of universalizing arguments, wedge arguments, arguments from authority, and arguments from analogy, arguing through thought experiments and allegories. In the light of the modern global turn, the (...) focus of research in the theory of reasoning and argumentation has shifted to the examination of how fundamental theses can be justified, as well as questions about how to engage in argumentation with people whose worldviews differ markedly from our own. Yet reasoning as a method of rational conviction still plays a decisive role in the comprehension, clarification, and critical questioning of one’s own positions, as well as that of others. (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)

Every artificial-intelligence research project needs a working definition of "intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, "intelligence" is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time. ;If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. ;NARS (...) uses a term-oriented formal language, characterized by the use of subject-predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. ;The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises. ;Since it has insufficient space-time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a "controlled concurrency" control mechanism, and a "bag-based" memory organization. ;A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. ;This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. ;Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed. (shrink)

Despite the innovation that evidence-based medicine (henceforth EBM) represents for biomedical sciences today, we argue that its strict evidential hierarchies do not faithfully represent the epistemic and practical reality of the evidence that should be used to make appropriate clinical decisions. We defend that it is necessary to make modifications to the methodology and models of clinical decision-making proposed by EBM and make them more sensitive to the use of different types of evidence and reasoning. Since various types of (...) evidence and reasoning, and medical expertise still play an irreducible role in clinical decision-making, rather than continuing with their strict hierarchies, we argue that those who subscribe to the EBM paradigm should discuss in much greater detail different types of evidence and reasoning. Admitting and advocating the role of other types of evidence and reasoning in clinical decision-making will also be necessary to introduce other types of inference and evaluation, as this generates conclusions that do not rely solely on induction or deduction. Both clinical judgment and the clinical eye play a prominent role in this context, particularly in diagnosis, as uncertainty inherently influences the process. If we hit the nail on the head, effective EBM practice requires other types of reasoning beyond biostatistical reasoning. With this in mind, we characterize clinical judgment based on abductive inference. (shrink)

In this paper we address the classical but still pending question regarding Thought Experiments: how can an imagined scenario bring new information or insight about the actual world? Our claim is that this general problem actually embraces two distinct questions: how can the creation of a just imagined scenario become functional to either a scientific or a philosophical research? and how can Thought Experiments hold a strong inferential power if their structures “do not seem to translate easily into standard forms (...) of deduction or induction”? :534–541, 1999). We contend that, in order to answer both questions, we should consider the relation between the creation of the imagined scenario and the inferential power of Thought Experiments. Specifically, we will analyze Thought Experiments from an eco-cognitive point of view as goal-oriented objects, explaining their inferential power considering their generation as the result of abductive cognition and the construction of an imagined scenario as a process of scientific modeling. This will lead us to consider the creation of a Thought Experiment as a case of sophisticated model-based abduction. (shrink)

Peirce is clearly America's candidate for a place on the roster of the great philosophers. The issues in the philosophy of science, epistemology and the philosophy of mind that were central to his philosophical project just happen to be ones that are still on the center stage on the contemporary philosophical scene. Peirce nowhere gives us a unified official exposition of his own theory of knowledge. We can ilustrate his opinions by the examination of three fundamental forms of inference: deduction, (...) induction and abduction. Demonstration of these kinds of inference is one of the ways how to explain Peirce's theory of scientific inquiry. (shrink)

Abductive reasoning involves finding the missing premise of an “unsaturated” deductive inference, thereby selecting a possible _explanans_ for a conclusion based on a set of previously accepted premises. In this paper, we explore abductive reasoning from a structural proof-theory perspective. We present a hybrid sequent calculus for classical propositional logic that uses sequents and antisequents to define a procedure for identifying the set of analytic hypotheses that a rational agent would be expected to select as _explanans_ (...) when presented with an abductive problem. Specifically, we show that this set may not include the deductively minimal hypothesis due to the presence of redundant information. We also establish that the set of all analytic hypotheses exhausts all possible solutions to the given problem. Finally, we propose a deductive criterion for differentiating between the best _explanans_ candidates and other hypotheses. (shrink)

This paper reviews the recent history of a subset of research in dynamical cognitive science, in particular that subset that allies itself with the sciences of complexity and casts cognitive systems as interaction dominant, noncomputational, and nonmodular. I look at this history in the light of C.S. Peirce's understanding of scientific reasoning as progressing from abduction to deduction to induction. In particular, I examine the development of a controversy concerning the use of the interaction dominance of human cognitive systems (...) as an explanation of the ubiquitous 1/f noise, multifractality, and complexity matching in human behavior. (shrink)

Accounts of analogical inference are usually categorized into four broad groups: abductive, deductive, inductive and sui generis. The purpose of this paper is to defend a sui generis model of analogical inference. It focuses on the sui generis account, as developed by Juthe [2005, 2009, 2015, 2016] and Botting’s [2017] criticism of it. This paper uses the pragmadialectical theory of argumentation as the methodological framework for analyzing and reconstructing argumentation. The paper has two main points. First, that Juthe’s arguments (...) against a deductive interpretation of prima facie analogy argumentation remain unaffected by Botting’s criticism, which means that many of the reasons against deductive reformulation of analogy argumentation still stand. The additional argument, which Botting himself brings up, that a deductive interpretation cannot account for the cumulative effect of analogies, just provides further reason to reject deductivism. The second main point of this paper is that an inductive interpretation of analogical inference also fails. There are constitutional differences between inductive and analogical inference that cannot be bridged. The result is a firm defense of the sui generis view of analogical inference. (shrink)

Mercier & Sperber (M&S) argue for their argumentative theory in terms of communicative abilities. Insights can be gained by extending the discussion beyond human reasoning to rodent and robot navigation. The selection of arguments and conclusions that are mutually reinforcing can be cast as a form of abductive reasoning that I argue underlies the construction of cognitive maps in navigation tasks.

Seldom does human reasoning fit the standards of deduction. Yet logicians have tended to use the strict standards of deductive validity for assessing all inferences. _Within Reason_ develops instead a way of assessing arguments and inferences that is directly appropriate to the non-deductive forms people regularly use. It uses analogy, and argument from analogy, to provide a thread that unites various forms: raising objections, inductions of various sorts, arguments to explanation, and arguments to action. The discussion is developed progressively, (...) at each stage building on the skills learned previously. And at each stage the book suggetss ways of assessing the strength of reasoning. Most of the examples are taken from the great texts of the Western tradition. In the exercises that form an integral part of each chapter an even broader range of writing is presented, offering instructors an extraordinary variety of texts at various levels ot assign to their students. The thesis implicit in the book is that induction, arguments to explanation and arguments to action are all sophisticated forms of arguments from analogy. Burbidge is concerned not simply to warn against the misuse of non-deductive reasoning but also to develop a more positive theory that allows us both to better udnerstand the workings of non-deductive reasoning, and to better employ it. (shrink)

While inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (...) by Task interaction indicated greater involvement of left inferior frontal gyrus in deduction than induction, while left dorsolateral prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning. (shrink)

We examine Charles S. Peirce's mature views on the logic of science, especially as contained in his later and still mostly unpublished writings. We focus on two main issues. The first concerns Peirce's late conception of retroduction. Peirce conceived inquiry as performed in three stages, which correspond to three classes of inferences: abduction or retroduction, deduction, and induction. The question of the logical form of retroduction, of its logical justification, and of its methodology stands out as the three major threads (...) in his later writings. The other issue concerns the second stage of scientific inquiry, deduction. According to Peirce's later formulation, deduction is divided not only into two kinds but also into two sub-stages: logical analysis and mathematical reasoning, where the latter is either corollarial or theorematic. Save for the inductive stage, which we do not address here, these points cover the essentials of Peirce's latest thinking on the l.. (shrink)

Forrester’s proposed seventh style of reasoning – thinking in cases – functions as an analogous, dyadic relationship that, whilst indebted philosophically to the logical reasoning and semiotics of Charles Peirce, is prone to creating feedback loops between induction and deduction, precluding novel abductive hypotheses from advancing medical knowledge. Reasoning with a Peircean triadic model opens up the contexts and methods of meaning-making and reasoning through medical cases, and the potent influence of their genre conventions, to (...) intellectual critical scrutiny. Vitally, it offers a third mode – abduction – that this article argues needs to be reintroduced into Forrester’s model of reasoning with cases. This article demonstrates this by applying a Peircean triadic model of reasoning to Forrester’s own model, tracing a shared genealogy but one in which the abductive element was lost. The article goes on to illustrate the explanatory and predictive potential of Peircean abductive reasoning and the necessary re-theorising of the case this entails. This argument is supported through an analysis of early case reports of what would become HIV/Aids, drawn from the Case Records of Massachusetts General Hospital series in the New England Journal of Medicine. (shrink)

This work proposes an understanding of deductive, default and abductive reasoning as different instances of the same phenomenon: epistemic dynamics. It discusses the main intuitions behind each one of these reasoning processes, and suggest how they can be understood as different epistemic actions that modify an agent’s knowledge and/or beliefs in a different way, making formal the discussion with the use of the dynamic epistemic logic framework. The ideas in this paper put the studied processes under the (...) same umbrella, thus highlighting their relationship and allowing a better understanding of how they interact together. (shrink)

Seldom does human reasoning fit the standards of deduction. Yet logicians have tended to use the strict standards of deductive validity for assessing all inferences. _Within Reason_ develops instead a way of assessing arguments and inferences that is directly appropriate to the non-deductive forms people regularly use. It uses analogy, and argument from analogy, to provide a thread that unites various forms: raising objections, inductions of various sorts, arguments to explanation, and arguments to action. The discussion is developed progressively, (...) at each stage building on the skills learned previously. And at each stage the book suggetss ways of assessing the strength of reasoning. Most of the examples are taken from the great texts of the Western tradition. In the exercises that form an integral part of each chapter an even broader range of writing is presented, offering instructors an extraordinary variety of texts at various levels ot assign to their students. The thesis implicit in the book is that induction, arguments to explanation and arguments to action are all sophisticated forms of arguments from analogy. Burbidge is concerned not simply to warn against the misuse of non-deductive reasoning but also to develop a more positive theory that allows us both to better udnerstand the workings of non-deductive reasoning, and to better employ it. (shrink)

We apply methods of abduction derived from propositional probabilistic reasoning to predicate probabilistic reasoning, in particular inductive logic, by treating finite predicate knowledge bases as potentially infinite propositional knowledge bases. It is shown that for a range of predicate knowledge bases and several key propositional inference processes this procedure is well defined, and furthermore yields an explanation for the validity of the induction in terms of 'reasons'.

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)

In this paper a new conception of foundation-oriented epistemology is developed. The major challenge for foundation-oriented justifications consists in the problem of stopping the justificational regress without taking recourse to dogmatic assumptions or circular reasoning. Two alternative accounts that attempt to circumvent this problem, coherentism and externalism, are critically discussed and rejected as unsatisfactory. It is argued that optimality arguments are a new type of foundation-oriented justification that can stop the justificational regress. This is demonstrated on the basis of (...) a novel result in the area of induction: the optimality of meta-induction. In the final section the method of optimality justification is generalized to deductive and abductive inferences. (shrink)