Different approaches analyze the strength of a natural language argument in different ways. This paper contrasts the dialectical, structural, probabilistic (or Bayesian), computational, and empirical approaches by exemplarily applying them to a single argumentative text (Epicureans on Squandering Life; Aikin & Talisse, 2019). Rather than pitching these approaches against one another, our main goal is to show the room for fruitful interaction. Our focus is on a dialectical analysis of the squandering argument as an argumentative response that voids an interlocutor’s (...) right to assertion. This analysis addresses the pragmatic dimensions of arguing and implies an argument structure that is consistent with empirical evidence of perceived argument strength. Results show that the squandering argument can be evaluated as a (non-fallacious) ad hominem argument, which however is not necessarily stronger than possible arguments attacking it. (shrink)

This article provides a brief selective overview and discussion of recent research into natural language argumentation that may inform the study of human reasoning on the assumption that an episode of argumentation issues an invitation to accept a corresponding inference. As this research shows, arguers typically seek to establish new consequences based on prior information. And they typically do so vis-à-vis a real or an imagined opponent, or an opponent-position, in ways that remain sensitive to considerations of context, audiences, and (...) goals. Deductively valid inferences remain a limiting case of such reasoning. In view of these insights, it may appear less surprising that allegedly “irrational” behavior can regularly be produced in experimental settings that expose subjects to standardized reasoning tasks. (shrink)

According to the argument scheme approach, to evaluate a given scheme-saturating instance completely does entail asking all critical questions relevant to it. Although this is a central task for argumentation theorists, the field currently lacks a method for providing a complete argument evaluation. Approaching this task at the meta-level, we combine a logical with a substantive approach to the argument schemes by starting from Toulmin’s schema: ‘data, warrant, so claim’. For the yet more general schema: ‘premise; if premise, then conclusion; (...) so conclusion’, we forward a meta-level CQ-list that is arguably both complete and applicable. This list should inform ongoing theoretical efforts at generating appropriate object-level CQs for specific argument schemes. (shrink)

Some arguments are good; others are not. How can we tell the difference? This article advances three proposals as a partial answer to this question. The proposals are keyed to arguments conditioned by different degrees of uncertainty: mild, where the argument’s premises are hedged with point-valued probabilities; moderate, where the premises are hedged with interval probabilities; and severe, where the premises are hedged with non-numeric plausibilities such as ‘very likely’ or ‘unconfirmed’. For mild uncertainty, the article proposes to apply a (...) principle referred to as ‘Jeffrey’s rule’, for the principle is a generalization of Jeffrey conditionalization. For moderate uncertainty, the proposal is to extend Jeffrey’s rule for use with probability intervals. For severe uncertainty, the article proposes that even when lack of probabilistic information prevents the application of Jeffrey’s rule, the rule can be adapted to these conditions with the aid of a suitable plausibility measure. Together, the three proposals introduce an approach to argument evaluation that complements established frameworks for evaluating arguments: deductive soundness, informal logic, argumentation schemes, pragma-dialectics, and Bayesian inference. Nevertheless, this approach can be looked at as a generalization of the truth and validity conditions of the classical criterion for sound argumentation. (shrink)

How to understand and assess arguments in which the popularity of an opinion is put forward as a reason to accept that opinion? There exist widely diverging views on how to analyse and evaluate such arguments from popularity. First, I define the concept of an argument from popularity, and show that typical appeals to the popularity of a policy are not genuine arguments from popularity. Second, I acknowledge the importance of some recent probability-based accounts according to which some arguments from (...) popularity are epistemically strong arguments, but also contend that despite these strengths such arguments have at most limited value in argumentative discussions. Finally, I show that there are at least five different ways that arguments from popularity can be fallacious, and examine what this means for an account of the Fallacy of Popularity. (shrink)

Many experiments suggest that participants are more critical of arguments that challenge their views or that come from untrustworthy sources. However, other results suggest that this might not be true of demonstrative arguments. A series of four experiments tested whether people are influenced by two factors when they evaluate demonstrative arguments: how confident they are in the answer being challenged by the argument, and how much they trust the source of the argument. Participants were not affected by their confidence in (...) the answer challenged by the argument. By contrast, they were sometimes affected by their trust in the argument’s source. Analyses of reaction times and transfer problems suggest that source trustworthiness did not directly affect argument evaluation, but affected instead the number of times the participants considered the arguments. Our results thus suggest that people can evaluate demonstrative arguments objectively. In conclusion, we defend the hypothesis that people might also be able to evaluate non-demonstrative arguments objectively. These results support the predictions of the argumentative theory of reasoning. (shrink)

While the role of discourse connectives has long been acknowledged in argumentative frameworks, these approaches often take a coarse-grained approach to connectives, treating them as a unified group having similar effects on argumentation. Based on an empirical study of the straw man fallacy, we argue that a more fine-grained approach is needed to explain the role of each connective and illustrate their specificities. We first present an original corpus study detailing the main features of four causal connectives in French that (...) speakers routinely use to attribute meaning to another speaker, which is a key element of straw man fallacies. We then assess the influence of each of these connectives in a series of controlled experiments. Our results indicate each connective has different effects for the persuasiveness of straw man fallacies, and that these effects can be explained by differences in their semantic profile, as evidenced in our corpus study. Taken together, our results demonstrate that connectives are important for argumentation but should be analyzed individually, and that the study of fallacies should include a fine-grained analysis of the linguistic elements typically used in their formulation. (shrink)

Decisions concerning proof of facts in criminal law must be rational because of what is at stake, but the decision‐making process must also be cognitively feasible because of cognitive limitations, and it must obey the relevant legal–procedural constraints. In this topic three approaches to rational reasoning about evidence in criminal law are compared in light of these demands: arguments, probabilities, and scenarios. This is done in six case studies in which different authors analyze a manslaughter case from different theoretical perspectives, (...) plus four commentaries on these case studies. The aim of this topic is to obtain more insight into how the different approaches can be applied in a legal context. This will advance the discussion on rational reasoning about evidence in law and will contribute more widely to cognitive science on a number of topics, including the value of probabilistic accounts of cognition and the problem of dealing with cognitive biases in reasoning under uncertainty in practical contexts. (shrink)

This article seeks, first, to show that much of the existing normative work on argument from expert opinion (AEO) is problematic for failing to be properly informed by empirical findings on expert performance. Second, it seeks to show how, with the analytic tool of Bayesian reasoning, the problem diagnosed can be remedied to circumvent some of the problems facing the scheme-based treatment of AEOs. To establish the first contention, we will illustrate how empirical studies on factors conditioning expert reliability can (...) be drawn upon to re-construct. Walton’s critical questions matching the scheme of AEOs. To establish the second contention, we will illustrate how Walton’s re-constructed set of critical questions can be formalized within a Bayesian network. Finally, we will highlight how the specific ways in which the Bayesian framework we propose is both continuous with and distinct from the models of source reliability put forward by theorists like Bovens and Hartmann (2003). (shrink)

The use of the journal impact factor (JIF) as a measure for the quality of individual manuscripts and the merits of scientists has faced significant criticism in recent years. We add to the current criticism in arguing that such an application of the JIF in policy and decision making in academia is based on false beliefs and unwarranted inferences. To approach the problem, we use principles of deductive and inductive reasoning to illustrate the fallacies that are inherent to using journal (...) based metrics for evaluating the work of scientists. In doing so, we elaborate that if we judge scientific quality based on the JIF or other journal based metrics we are either guided by invalid or weak arguments or in fact consider our uncertainty about the quality of the work and not the quality itself. (shrink)

ABSTRACTIn everyday situations, people regularly receive information from large groups of people and from single experts. Although lay opinions and expert opinions have been studied extensively in isolation, the present study examined the relationship between the two by asking how many laypeople are needed to counter an expert opinion. A Bayesian formalisation allowed the prescription of this quantity. Participants were subsequently asked to assess how many laypeople are needed in different situations. The results demonstrate that people are sensitive to the (...) relevant factors identified for determining how many lay opinions are required to counteract a single expert opinion. People's assessments were fairly good in line with Bayesian predictions. (shrink)

This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and (...) may indeed serve to correct, the informal understanding and applications of the RSA criteria concerning their conceptual dependence, their function as update-thresholds, and their status as obligatory rather than permissive norms, but also show how these formal and informal normative approachs can in fact align. (shrink)

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than (...) standard Bayesian conditionalization, is able to characterise the special value of logically valid argument schemes in uncertain reasoning contexts, greatly extends the range of inferences and argumentative phenomena that can be adequately described in a Bayesian framework, and undermines some influential theoretical motivations for dual function models of human cognition. We conclude that the probabilistic norms given by the Bayesian approach to rationality are not necessarily at odds with the norms given by classical logic. Rather, the Bayesian theory of argumentation can be seen as justifying and enriching the argumentative norms of classical logic. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Bayes nets are a powerful tool for researchers in statistics and artificial intelligence. This chapter demonstrates that they are also of much use for philosophers and psychologists interested in (Bayesian) rationality. To do so, we outline the general methodology of Bayes nets modeling in rationality research and illustrate it with several examples from the philosophy and psychology of reasoning and argumentation. Along the way, we discuss the normative foundations of Bayes nets modeling and address some of the methodological problems it (...) raises. (shrink)

Throughout this paper, we are trying to show how and why our Mathematical frame-work seems inappropriate to solve problems in Theory of Computation. More exactly, the concept of turning back in time in paradoxes causes inconsistency in modeling of the concept of Time in some semantic situations. As we see in the first chapter, by introducing a version of “Unexpected Hanging Paradox”,first we attempt to open a new explanation for some paradoxes. In the second step, by applying this paradox, it (...) is demonstrated that any formalized system for the Theory of Computation based on Classical Logic and Turing Model of Computation leads us to a contradiction. We conclude that our mathematical frame work is inappropriate for Theory of Computation. Furthermore, the result provides us a reason that many problems in Complexity Theory resist to be solved.(This work is completed in 2017 -5- 2, it is in vixra in 2017-5-14, presented in Unilog 2018, Vichy). (shrink)

Douglas Walton’s multitudinous contributions to the study of argumentation seldom, if ever, directly engage with argumentation in mathematics. Nonetheless, several of the innovations with which he is most closely associated lend themselves to improving our understanding of mathematical arguments. I concentrate on two such innovations: dialogue types (§1) and argumentation schemes (§2). I argue that both devices are much more applicable to mathematical reasoning than may be commonly supposed.