Similarity, precedent and argument from analogy

In this paper, it is shown (1) that there are two schemes for argument from analogy that seem to be competitors but are not, (2) how one of them is based on a distinctive type of similarity premise, (3) how to analyze the notion of similarity using story schemes illustrated by some cases, (4) how arguments from precedent are based on arguments from analogy, and in many instances arguments from classification, and (5) that when similarity is defined by means of episode schemes, we can get a clearer idea of how it integrates with the use of argument from classification and argument from precedent in case-based reasoning by using a dialogue structure

Formalization of the ad hominem argumentation scheme

In this paper, several examples from the literature, and one central new one, are used as case studies of texts of discourse containing an argumentation scheme that has now been widely investigated in literature on argumentation. Argumentation schemes represent common patterns of reasoning used in everyday conversational discourse. The most typical ones represent defeasible arguments based on nonmonotonic reasoning. Each scheme has a matching set of critical questions used to evaluate a particular argument fitting that scheme. The project is to study how to build a formal computational model of this scheme for the circumstantial ad hominem argument using argumentation tools and systems developed in artificial intelligence. It is shown how the formalization built using these tools is applicable to the tasks of identification, analysis and evaluation of the central case studied. One important implication of the work is that it provides a foundational basis for showing how other argumentation schemes can be formalized

Assessing relevance

This paper advances an approach to relevance grounded on patterns of material inference called argumentation schemes, which can account for the reconstruction and the evaluation of relevance relations. In order to account for relevance in different types of dialogical contexts, pursuing also non-cognitive goals, and measuring the scalar strength of relevance, communicative acts are conceived as dialogue moves, whose coherence with the previous ones or the context is represented as the conclusion of steps of material inferences. Such inferences are described using argumentation schemes and are evaluated by considering 1) their defeasibility, and 2) the acceptability of the implicit premises on which they are based. The assessment of both the relevance of an utterance and the strength thereof depends on the evaluation of three interrelated factors: 1) number of inferential steps required; 2) the types of argumentation schemes involved; and 3) the implicit premises required

Building bridges between doctors and patients: the design and pilot evaluation of a training session in argumentation for chronic pain experts

Shared decision-making requires doctors to be competent in exchanging views with patients to identify the appropriate course of action. In this paper we focus on the potential of a course in argumentation as a promising way to empower doctors in presenting their viewpoints and addressing those of patients. Argumentation is the communication process in which the speaker, through the use of reasons, aims to convince the interlocutor of the acceptability of a viewpoint. The value of argumentation skills for doctors has been addressed in the literature. Yet, there is no research on what a course on argumentation might look like. In this paper, we present the content and format of a training session in argumentation for doctors and discuss some insights gained from a pilot study that examined doctors' perceived strengths and limitations vis-à-vis this training

Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian network

In legal cases, stories or scenarios can serve as the context for a crime when reasoning with evidence. In order to develop a scientifically founded technique for evidential reasoning, a method is required for the representation and evaluation of various scenarios in a case. In this paper the probabilistic technique of Bayesian networks is proposed as a method for modeling narrative, and it is shown how this can be used to capture a number of narrative properties. Bayesian networks quantify how the variables in a case interact. Recent research on Bayesian networks applied to legal cases includes the development of a list of legal idioms: recurring substructures in legal Bayesian networks. Scenarios are coherent presentations of a collection of states and events, and qualitative in nature. A method combining the quantitative, probabilistic approach with the narrative approach would strengthen the tools to represent and evaluate scenarios. In a previous paper, the development of a design method for modeling multiple scenarios in a Bayesian network was initiated. The design method includes two narrative idioms: the scenario idiom and the merged scenarios idiom. In this current paper, the method of Vlek, et al. (2013) is extended with a subscenario idiom and it is shown how the method can be used to represent characteristic features of narrative

“Whiteboxing” the Content of a Formal Mathematical Text in a Dynamic Geometry Environment

In this article, we provide an empirical example of how digital technology; in this case, GeoGebra may assist students in uncovering—or whiteboxing—the content of a mathematical proof, in this case that of Proposition 41 from Euclid’s Elements. In the discussion of the example, we look into the impact of GeoGebra’s “dragging” functionality on students’ interactions and the possession and development of students’ proof schemes. The study and accompanying analysis illustrate that, despite the positive whiteboxing effects in relation to the mathematical content of the proposition, whiteboxing through dragging calls for caution in relation to students’ work with proof and proving—in particular, in relation to students seeing the necessity for formal proof. Moreover, caution must be paid, e.g., by teachers, so that students do not jump to conclusions and in the process develop inexpedient mathematical proof schemes upon which they may stumble in their future mathematical work