(1994). Inductive reasoning, deductive reasoning and mental models. International Studies in the Philosophy of Science: Vol. 8, No. 1, pp. 65-67. doi: 10.1080/02698599408573483.

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 (...) class='Hi'>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)

A number of single- and dual-process theories provide competing explanations as to how reasoners evaluate conditional arguments. Some of these theories are typically linked to different instructions—namely deductive and inductive instructions. To assess whether responses under both instructions can be explained by a single process, or if they reflect two modes of conditional reasoning, we re-analysed four experiments that used both deductive and inductive instructions for conditional inference tasks. Our re-analysis provided evidence consistent with a single process. (...) In two new experiments we established a double dissociation of deductive and inductive instructions when validity and plausibility of conditional problems were pitted against each other. This indicates that at least two processes contribute to conditional reasoning. We conclude that single-process theories of conditional reasoning cannot explain the observed results. Theories that postulate at least two processes are needed to account for our findings. (shrink)

Deductive reasoning is the kind of reasoning in which, roughly, the truth of the input propositions (the premises) logically guarantees the truth of the output proposition (the conclusion), provided that no mistake has been made in the reasoning. The premises may be propositions that the reasoner believes or assumptions that the reasoner is exploring. Deductive reasoning contrasts with inductive reasoning, the kind of reasoning in which the truth of the premises need not (...) guarantee the truth of the conclusion. (shrink)

The paper aims at the rehabilitation of deductive reasoning. As a paradigm of reliable reasoning, it should be applicable in every confirmation context. In particular, it should transmit inductive justification, so that if D justifies a hypothesis H, then D also justifies all deductive conclusions from H. Nevertheless, most current philosophers of science reject such a transmission principle as false. They argue against it by providing apparent counter-examples and also by showing that it is incompatible with (...) common confirmation theories such as HD-confirmation and Bayesianism. I argue in the opposite direction that we should stick to the transmission principle and revise instead our justification theories towards more cautious justification procedures that respect the transmission principle. This will avoid further paradoxes of these theories and, in particular, will enable us to apply our confirmed hypotheses to new situations in a well-founded way. (shrink)

This print edition of Read's account of logical thought includes the original publication's diagrams and tables. In this excellent book, Read commences by offering an overview of past attitudes and definitions of logic. Individual chapters consider the various means by which logical processes are conceived and developed in the mind. Philosophical arguments, spatial reasoning and mathematical forms of logic are discussed in great depth, with illustrations appended where deemed necessary. Read, an academic and philosopher, employs his decades long experience (...) of teaching students about the logical disciplines to write a detailed and comprehensive accounts of logic's aspects. Throughout this work, he attempts to retain a clarity of expression which makes the concepts discussed understandable for readers previously unversed in the types of logical expression. Read also sets out the principles behind setting up a hypothesis, and highlights the common logical fallacies which crop up in human reasoning. Extensive attention is given to the process of syllogism, by which sound conclusions are drawn from the premises offered. Throughout the book, Read is attentive in defining and explaining the esoteric terminology native to logic. As well as allowing the reader swift comprehension of the various subtopics, this explanatory style confers improved flow to the book. At the conclusion we are offered many questions corresponding to each chapter: these are intended to test the reader's understanding, and enforce the mental habits of utilising logical and critical thought. First published in 1898 and subject to several editions with revisions and refinements thereafter, Logic Deductive and Inductive retains a good reputation among academics and logicians. Its successful balance between striking a concise introductory tone with an embrace of the core elements of the subject has served to preserve its popularity into the modern day. (shrink)

The study of deductive reasoning has been a major paradigm in psychology for approximately the past 40 years. Research has shown that people make many logical errors on such tasks and are strongly influenced by problem content and context. It is argued that this paradigm was developed in a context of logicist thinking that is now outmoded. Few reasoning researchers still believe that logic is an appropriate normative system for most human reasoning, let alone a model (...) for describing the process of human reasoning, and many use the paradigm principally to study pragmatic and probabilistic processes. It is suggested that the methods used for studying reasoning be reviewed, especially the instructional context, which necessarily defines pragmatic influences as biases. (shrink)

Clinical trial results are often interpreted by inductive reasoning, in a trial design-limited manner, directed toward modifications of the current clinical practice. Deductive reasoning is an alternative in which results of relevant trials are combined in indisputable premises that lead to a conclusion easily testable in future trials.

Mathematics used to be portrayed as a deductive science. Stemming from Polya, however, is a philosophical movement which broadens the concept of mathematical reasoning to include inductive or quasi-empirical methods. Interest in inductive methods is a welcome turn from foundationalism toward a philosophy grounded in mathematical practice. Regrettably, though, the conception of mathematical reasoning embraced by quasi-empiricists is still too narrow to include the sort of thought-experiment which Mueller describes as traditional mathematical proof and which Lakatos examines (...) in Proofs and refutations. This paper extends the concept of mathematical reasoning along two further dimensions to accommodate thought-experiment.Keywords: Thought-experiment; Informal proof; Mathematical reasoning. (shrink)

In this paper we deal with two types of reasoning: induction, and deduction First, we present a unified computational model of deductive reasoning through models, where deduction occurs in five phases: Construction, Integration, Conclusion, Falsification, and Response. Second, we make an attempt, to analyze induction through the same phases. Our aim is an explorative evaluation of the mental processes possibly shared by deductive and inductive reasoning.

Probability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through information or experience; induction and belief change are one and same topic, after all. The most basic of these rules is simply to conditionalize with respect to the information received; and there are similar and more general rules. 1 Hence, a fundamental reason for the epistemological success (...) of probability theory is that there at all exists a well-behaved concept of conditional probability. Still, people have, and have reasons for, various concerns over probability theory. One of these is my starting point: Intuitively, we have the notion of plain belief; we believe propositions2 to be true (or to be false or neither). Probability theory, however, offers no formal counterpart to this notion. Believing A is not the same as having probability 1 for A, because probability 1 is incorrigible3; but plain belief is clearly corrigible. And believing A is not the same as giving A a probability larger than some 1 - c, because believing A and believing B is usually taken to be equivalent to believing A & B.4 Thus, it seems that the formal representation of plain belief has to take a non-probabilistic route. Indeed, representing plain belief seems easy enough: simply represent an epistemic state by the set of all propositions believed true in it or, since I make the common assumption that plain belief is deductively closed, by the conjunction of all propositions believed true in it. But this does not yet provide a theory of induction, i.e. an answer to the question how epistemic states so represented are changed tbrough information or experience. There is a convincing partial answer: if the new information is compatible with the old epistemic state, then the new epistemic state is simply represented by the conjunction of the new information and the old beliefs. This answer is partial because it does not cover the quite common case where the new information is incompatible with the old beliefs. It is, however, important to complete the answer and to cover this case, too; otherwise, we would not represent plain belief as conigible. The crucial problem is that there is no good completion. When epistemic states are represented simply by the conjunction of all propositions believed true in it, the answer cannot be completed; and though there is a lot of fruitful work, no other representation of epistemic states has been proposed, as far as I know, which provides a complete solution to this problem. In this paper, I want to suggest such a solution. In [4], I have more fully argued that this is the only solution, if certain plausible desiderata are to be satisfied. Here, in section 2, I will be content with formally defining and intuitively explaining my proposal. I will compare my proposal with probability theory in section 3. It will turn out that the theory I am proposing is structurally homomorphic to probability theory in important respects and that it is thus equally easily implementable, but moreover computationally simpler. Section 4 contains a very brief comparison with various kinds of logics, in particular conditional logic, with Shackle's functions of potential surprise and related theories, and with the Dempster - Shafer theory of belief functions. (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)

Chemical visualizations and models are special kinds of situated, inductive arguments. In this paper, I examine several historical case studies—an archive of images from museums, special collections, and popular magazines—as examples of emergent practices of physical modeling as theoretical play which became the basis for molecular biology and structural chemistry. Specifically, I trace a legacy of visualization tools that starts with Archibald Scott Cooper and Friedrich Kekulé in the late 1800s, crystallizes as material manipulatives in Kekulé’s student Jacobus Henricus Van’t (...) Hoff and his folded paper “toys,” is legitimized in the California lab of Linus Pauling, and is glorified in the popular imaginary with James Watson and Francis Crick’s model of DNA. My tracing then follows several threads into contemporary modeling practices. I ultimately argue that modeling play, originally outside of the boundary of deductive, positivist science, is now an accepted mode of inductive reasoning in these related chemical fields. (shrink)

This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. (...) The theory predicts several phenomena of reasoning about absolute probabilities, including typical biases. It correctly predicts certain cognitive illusions in inferences about relative probabilities. It accommodates reasoning based on numerical premises, and it explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem. Finally, it dispels some common misconceptions of probabilistic reasoning. (shrink)

This chapter offers a short logic lesson as an introduction to what philosophers and other critical thinkers do when they offer and criticize arguments. Logic is the study of the principles of correct reasoning associated with the formation and analysis of arguments. The creators of South Park, for the most part, know these logical principles. They purposely violate them, though, to show the absurdities contained in certain beliefs, opinions, ideas, and arguments. In fact, much of South Park's humor concerns (...) logical violations and the absurdities, contradictions, and problems that result. The authors consider some basics of logic using examples from South Park episodes, and show some differences between correct and incorrect reasoning. There are two main kinds of arguments, deductive arguments and inductive arguments. The authors insist on the need to form good arguments, and the need to evaluate the arguments of others. (shrink)

In his Logic, Pierre Gassendi proposes that our inductive inferences lack the information we would need to be certain of the claims that they suggest. Not even deductivist inference can insure certainty about empirical claims because the experientially attained premises with which we adduce support for such claims are no greater than probable. While something is surely amiss in calling deductivist inference "probabilistic," it seems Gassendi has hit upon a now-familiar, sensible point—namely, the use of deductive reasoning in (...) empirical contexts, while providing certain formal guarantees, does not insulate empirical arguments from judgment by the measure of belief which we invest in their premises. The more general point, which distinguishes Gassendi among his contemporaries, is that the strength shared by all empirical claims consists in the warrant from experience for those claims we introduce in their support. (shrink)

I set up two axiomatic theories of inductive support within the framework of Kolmogorovian probability theory. I call these theories ‘Popperian theories of inductive support’ because I think that their specific axioms express the core meaning of the word ‘inductive support’ as used by Popper (and, presumably, by many others, including some inductivists). As is to be expected from Popperian theories of inductive support, the main theorem of each of them is an anti-induction theorem, the stronger one of them saying, (...) in fact, that the relation of inductive support is identical with the empty relation. It seems to me that an axiomatic treatment of the idea(s) of inductive support within orthodox probability theory could be worthwhile for at least three reasons. Firstly, an axiomatic treatment demands from the builder of a theory of inductive support to state clearly in the form of specific axioms what he means by ‘inductive support’. Perhaps the discussion of the new anti-induction proofs of Karl Popper and David Miller would have been more fruitful if they had given an explicit definition of what inductive support is or should be. Secondly, an axiomatic treatment of the idea(s) of inductive support within Kolmogorovian probability theory might be accommodating to those philosophers who do not completely trust Popperian probability theory for having theorems which orthodox Kolmogorovian probability theory lacks; a transparent derivation of anti-induction theorems within a Kolmogorovian frame might bring additional persuasive power to the original anti-induction proofs of Popper and Miller, developed within the framework of Popperian probability theory. Thirdly, one of the main advantages of the axiomatic method is that it facilitates criticism of its products: the axiomatic theories. On the one hand, it is much easier than usual to check whether those statements which have been distinguished as theorems really are theorems of the theory under examination. On the other hand, after we have convinced ourselves that these statements are indeed theorems, we can take a critical look at the axioms—especially if we have a negative attitude towards one of the theorems. Since anti-induction theorems are not popular at all, the adequacy of some of the axioms they are derived from will certainly be doubted. If doubt should lead to a search for alternative axioms, sheer negative attitudes might develop into constructive criticism and even lead to new discoveries. -/- I proceed as follows. In section 1, I start with a small but sufficiently strong axiomatic theory of deductive dependence, closely following Popper and Miller (1987). In section 2, I extend that starting theory to an elementary Kolmogorovian theory of unconditional probability, which I extend, in section 3, to an elementary Kolmogorovian theory of conditional probability, which in its turn gets extended, in section 4, to a standard theory of probabilistic dependence, which also gets extended, in section 5, to a standard theory of probabilistic support, the main theorem of which will be a theorem about the incompatibility of probabilistic support and deductive independence. In section 6, I extend the theory of probabilistic support to a weak Popperian theory of inductive support, which I extend, in section 7, to a strong Popperian theory of inductive support. In section 8, I reconsider Popper's anti-inductivist theses in the light of the anti-induction theorems. I conclude the paper with a short discussion of possible objections to our anti-induction theorems, paying special attention to the topic of deductive relevance, which has so far been neglected in the discussion of the anti-induction proofs of Popper and Miller. (shrink)

The resolving of the main problem of quantum mechanics about how a quantum leap and a smooth motion can be uniformly described resolves also the problem of how a distribution of reliable data and a sequence of deductive conclusions can be uniformly described by means of a relevant wave function “Ψdata”.

Ever since Aristotle it has been accepted that there exists a combination of inductive and deductive reasoning and a sort of unified inductive-deductive methodology. If one analyzes the procedures and logic of scientific explanation and the methods of generating and justifying scientific knowledge, one recognizes the prototype of philosophy of science found in Aristotle’s inductive and deductive procedure that is described in his Posterior Analytics, Physics and Metaphysics, where heviewed scientific inquiry as a progression from observations (...) to general principles and back to observation. He maintained that the scientist should induce explanatory principles from the phenomena to be explained, and then deduce statements about the phenomena from premisses which include these principles. (shrink)

Ever since Aristotle it has been accepted that there exists a combination of inductive and deductive reasoning and a sort of unified inductive-deductive methodology. If one analyzes the procedures and logic of scientific explanation and the methods of generating and justifying scientific knowledge, one recognizes the prototype of philosophy of science found in Aristotle’s inductive and deductive procedure that is described in his Posterior Analytics, Physics and Metaphysics, where heviewed scientific inquiry as a progression from observations (...) to general principles and back to observation. He maintained that the scientist should induce explanatory principles from the phenomena to be explained, and then deduce statements about the phenomena from premisses which include these principles. (shrink)

Mathematicians often speak of conjectures, yet unproved, as probable or well-confirmed by evidence. The Riemann Hypothesis, for example, is widely believed to be almost certainly true. There seems no initial reason to distinguish such probability from the same notion in empirical science. Yet it is hard to see how there could be probabilistic relations between the necessary truths of pure mathematics. The existence of such logical relations, short of certainty, is defended using the theory of logical probability (or objective Bayesianism (...) or non-deductive logic), and some detailed examples of its use in mathematics surveyed. Examples of inductive reasoning in experimental mathematics are given and it is argued that the problem of induction is best appreciated in the mathematical case. (shrink)

This interdisciplinary work is a collection of major essays on reasoning: deductive, inductive, abductive, belief revision, defeasible, cross cultural, conversational, and argumentative. They are each oriented toward contemporary empirical studies. The book focuses on foundational issues, including paradoxes, fallacies, and debates about the nature of rationality, the traditional modes of reasoning, as well as counterfactual and causal reasoning. It also includes chapters on the interface between reasoning and other forms of thought. In general, this last (...) set of essays represents growth points in reasoning research, drawing connections to pragmatics, cross-cultural studies, emotion and evolution. (shrink)

Scholars have widely discussed the paradigm of integration of Islam and science. Still, a model of integration of Islam and science that uses the reasoning of Martabat Tujuh [seven grades] has not been found. Therefore, this study sought to investigate the manuscripts of Shaykh Abdussamad al-Palimbani’s Martabat Tujuh in the integration of Islam and science. This study used a descriptive qualitative approach, and data collection techniques were documentation and interviews. This study found two spiritual reasons in the manuscript of (...) Shaykh Abdussamad al-Palimbani’s Martabat Tujuh: tanazzul [move down] deductive reasoning and taraqqi [move up] inductive reasoning. Contribution: The article contributed to developing the integration of Islam and science in Indonesia. The manuscripts of Martabat Tujuh [seven grades] by Shaykh Abdussamad al-Palimbani offer the circular motion of Martabat Tujuh as a model for integrating knowledge. The circular motion model of Martabat Tujuh is expected to be used as a theoretical basis for applying knowledge integration at various Islamic universities in Indonesia. (shrink)

Scholars have widely discussed the paradigm of integration of Islam and science. Still, a model of integration of Islam and science that uses the reasoning of Martabat Tujuh [seven grades] has not been found. Therefore, this study sought to investigate the manuscripts of Shaykh Abdussamad al-Palimbani’s Martabat Tujuh in the integration of Islam and science. This study used a descriptive qualitative approach, and data collection techniques were documentation and interviews. This study found two spiritual reasons in the manuscript of (...) Shaykh Abdussamad al-Palimbani’s Martabat Tujuh: tanazzul [move down] deductive reasoning and taraqqi [move up] inductive reasoning.Contribution: The article contributed to developing the integration of Islam and science in Indonesia. The manuscripts of Martabat Tujuh [seven grades] by Shaykh Abdussamad al-Palimbani offer the circular motion of Martabat Tujuh as a model for integrating knowledge. The circular motion model of Martabat Tujuh is expected to be used as a theoretical basis for applying knowledge integration at various Islamic universities in Indonesia. (shrink)

The growing trend of required ethics instruction in the business school curriculum has created a need for relevant teaching materials. In response to this need the Journal of Business Ethics is introducing a new case section. This section provides a forum for publishing and accessing a range of materials that can be used in teaching business ethics. This article discusses how business ethics cases can facilitate the development of deductive, inductive and critical reasoning skills.

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

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, discovery, and (...) explanation. This approach explains why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts. (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)

I suggest psychologists would more profitably study a totally different area of human reasoning than is discussed in the target article – the inductive reasoning people use in their everyday life that matters in consequential real-life decision making, rather than the deductive reasoning that psychologists have studied meticulously but that has relatively less ecological relevance to people's lives.

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)

This book represents the first major attempt by any author to provide an integrated account of the evidence for bias in human reasoning across a wide range of disparate psychological literatures. The topics discussed involve both deductive and inductive reasoning as well as statistical judgement and inference. In addition, the author proposes a general theoretical approach to the explanations of bias and considers the practical implications for real world decision making. The theoretical stance of the book is (...) based on a distinction between preconscious heuristic processes which determine the mental representation of 'relevant' features of the problem content, and subsequent analytic reasoning processes which generate inferences and judgements. Phenomena discussed and interpreted within this framework include feature matching biases in propositional reasoning, confirmation bias, biasing and debiasing effects of knowledge on reasoning, and biases in statistical judgement normally attributed to 'availability' and 'representativeness' heuristics. In the final chapter, the practical consequences of bias for real life decision making are considered, together with various issues concerning the problem of 'debiasing'. The major approaches discussed are those involving education and training on the one hand, and the development of intelligent software and interactive decision aids on the other. (shrink)

This essay is basically divided into two parts. The first deals with the similarities between reductio ad absurdum arguments and slippery slope arguments. The chief example comes from Thomas Hobbes’s Leviathan, which advances an argument for the necessity of government for humane living. The second addresses some pedagogical concerns centered around another pair of arguments: the argument by complete enumeration and the argument by inductive generalization. The illustration for this pair comes from the arts. I finish with a suggestion that (...) pairs like the above can be as effectively used in shorter, non-regular critical reasoning or introductory logic courses as those in mid-term or summer courses. Such pairing can demonstrate a good use of mixing and matching deductive and non-deductive arguments in teaching logic. (shrink)

In this paper, I explore a question about deductive reasoning: why am I in a position to immediately infer some deductive consequences of what I know, but not others? I show why the question cannot be answered in the most natural ways of answering it, in particular in Descartes’s way of answering it. I then go on to introduce a new approach to answering the question, an approach inspired by Hume’s view of inductive reasoning.

This text introduces university students to the philosophical ethos of critical thinking, as well as to the essential skills required to practice it. The authors believe that Critical Thinking should engage students with issues of broader philosophical interest while they develop their skills in reasoning and argumentation. The text is informed throughout by philosophical theory concerning argument and communication—from Aristotle's recognition of the importance of evaluating argument in terms of its purpose to Habermas's developing of the concept of communicative (...) rationality. The authors' treatment of the topic is also sensitive to the importance of language and of situation in shaping arguments, and to the necessity in argument of some interplay between reason and emotion. Unlike many other texts in this area, then, Good Reasons for Better Arguments helps to explain both why argument is important and how the social role of argument plays an important part in determining what counts as a good argument. If this text is distinctive in the extent to which it deals with the theory and the values of critical thinking, it is also noteworthy for the thorough grounding it provides in the skills of deductive and inductive reasoning; the authors present the reader with useful tools for the interpretation, evaluation and construction of arguments. A particular feature is the inclusion of a wide range of exercises, rich with examples that illuminate the practice of argument for the student. Many of the exercises are self testing, with answers provided at the back of the text; others are appropriate for in-class discussion and assignments. Challenging yet accessible, Good Reasons for Better Arguments brings a fresh perspective to an essential subject. (shrink)

In Reconstructing Reason and Representation, Murray Clarke offers a detailed study of the philosophical implications of evolutionary psychology. In doing so, he offers new solutions to key problems in epistemology and philosophy of mind, including misrepresentation and rationality. He proposes a naturalistic approach to reason and representation that is informed by evolutionary psychology, and, expanding on the massive modularity thesis advanced in work by Leda Cosmides and John Tooby, argues for a modular, adapticist account of misrepresentation and knowledge. Just as (...) the reliability of representation can be defended on the basis of an account of the proper function of cognitive modularity, misrepresentation can be explained through an appeal to the "gap theory," by noting the divergence between the proper and actual domains of cognitive modules in a massively modular mind.Clarke argues for an externalist, modular reliabilism by suggesting that evolution has equipped us with generally reliable inferential systems even if they do not always produce true beliefs. He argues that reliable deductive and inductive inference occurs only when cognitive modules deal with actual domains that are sufficiently similar to their proper domains. This psychologically informed, naturalized adapticism leads to the suggestion that knowledge is a set of natural kinds housed in the modules of a massively modular mind. Typically, the proper function of these cognitive modules is to provide us with truths that enable us to satisfy our basic biological needs. Beyond reasoning modules, other cognitive modules discussed include the ability to orient ourselves in space, and our abilities with language, numbers, object reasoning, and social understanding. Clarke also defends Cosmides and Tooby's massive modularity hypothesis against such critics as Jerry Fodor by demonstrating that these critics consistently misrepresent Cosmides and Tooby's position. (shrink)

Future Logic is an original, and wide-ranging treatise of formal logic. It deals with deduction and induction, of categorical and conditional propositions, involving the natural, temporal, extensional, and logical modalities. Traditional and Modern logic have covered in detail only formal deduction from actual categoricals, or from logical conditionals (conjunctives, hypotheticals, and disjunctives). Deduction from modal categoricals has also been considered, though very vaguely and roughly; whereas deduction from natural, temporal and extensional forms of conditioning has been all but totally ignored. (...) As for induction, apart from the elucidation of adductive processes (the scientific method), almost no formal work has been done. This is the first work ever to strictly formalize the inductive processes of generalization and particularization, through the novel methods of factorial analysis, factor selection and formula revision. This is the first work ever to develop a formal logic of the natural, temporal and extensional types of conditioning (as distinct from logical conditioning), including their production from modal categorical premises. Future Logic contains a great many other new discoveries, organized into a unified, consistent and empirical system, with precise definitions of the various categories and types of modality (including logical modality), and full awareness of the epistemological and ontological issues involved. Though strictly formal, it uses ordinary language, wherever symbols can be avoided. Among its other contributions: a full list of the valid modal syllogisms (which is more restrictive than previous lists); the main formalities of the logic of change (which introduces a dynamic instead of merely static approach to classification); the first formal definitions of the modal types of causality; a new theory of class logic, free of the Russell Paradox; as well as a critical review of modern metalogic. But it is impossible to list briefly all the innovations in logical science — and therefore, epistemology and ontology — this book presents; it has to be read for its scope to be appreciated. (shrink)

Here the author of How to Solve It explains how to become a "good guesser." Marked by G. Polya's simple, energetic prose and use of clever examples from a wide range of human activities, this two-volume work explores techniques of guessing, inductive reasoning, and reasoning by analogy, and the role they play in the most rigorous of deductive disciplines.

2014 Reprint of 1954 American Edition. Full facsimile of the original edition, not reproduced with Optical Recognition Software. This two volume classic comprises two titles: "Patterns of Plausible Inference" and "Induction and Analogy in Mathematics." This is a guide to the practical art of plausible reasoning, particularly in mathematics, but also in every field of human activity. Using mathematics as the example par excellence, Polya shows how even the most rigorous deductive discipline is heavily dependent on techniques of (...) guessing, inductive reasoning, and reasoning by analogy. In solving a problem, the answer must be guessed at before a proof can be given, and guesses are usually made from a knowledge of facts, experience, and hunches. The truly creative mathematician must be a good guesser first and a good prover afterward; many important theorems have been guessed but no proved until much later. In the same way, solutions to problems can be guessed, and a god guesser is much more likely to find a correct solution. This work might have been called "How to Become a Good Guesser."-From the Dust Jacket. (shrink)

'You shouldn't drink too much. The Earth is round. Milk is good for your bones.' Are any of these claims true? How can you tell? Can you ever be certain you are right? For anyone tackling philosophical logic and critical thinking for the first time, Critical Thinking: An Introduction to Reasoning Well provides a practical guide to the skills required to think critically. From the basics of good reasoning to the difference between claims, evidence and arguments, Robert Arp (...) and Jamie Carlin Watson cover the topics found in an introductory course. Now revised and fully updated, this Second Edition features a glossary, chapter summaries, more student-friendly exercises, study questions, diagrams, and suggestions for further reading. Topics include: the structure, formation, analysis and recognition of arguments deductive validity and soundness inductive strength and cogency inference to the best explanation truth tables tools for argument assessment informal and formal fallacies With real life examples, advice on graduate school entrance exams and an expanded companion website packed with additional exercises, an answer key and help with real life examples, this easy-to-follow introduction is a complete beginner's tool set to good reasoning, analyzing and arguing. Ideal for students in basic reasoning courses and students preparing for graduate school. (shrink)

Mathematical instrumentalism construes some parts of mathematics, typically the abstract ones, as an instrument for establishing statements in other parts of mathematics, typically the elementary ones. Gödel’s second incompleteness theorem seems to show that one cannot prove the consistency of all of mathematics from within elementary mathematics. It is therefore generally thought to defeat instrumentalisms that insist on a proof of the consistency of abstract mathematics from within the elementary portion. This article argues that though some versions of mathematical instrumentalism (...) are defeated by Gödel’s theorem, not all are. By considering inductive reasons in mathematics, we show that some mathematical instrumentalisms survive the theorem. (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)