The first edition of this book profoundly challenged and divided students of philosophy, sociology, and the history of science when it was published in 1976. In this second edition, Bloor responds in a substantial new Afterword to the heated debates engendered by his book.

Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...) evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. (shrink)

Argument quality has consistently been shown to have strong and lasting persuasive effects. The question is what criteria people use to distinguish strong from weak arguments and how these criteria relate to the ones proposed in normative argumentation theory. In an experiment 235 participants without training in argumentation theory rated the acceptance of 30 claims about the desirability of a consequence that were supported by either an argument from analogy, from authority, or from consequences. The supporting arguments were systematically manipulated (...) to violate argument type specific criteria. Participants proved sensitive to the violation of most, but not all, argument type specific criteria. From a normative perspective these findings suggest that people act in a fairly adequate way. These findings also enable a more precise description of what people may do when critically appraising arguments, which has important implications for the use of argument quality as a methodological tool in persuasion research. (shrink)

Interaction mining is about discovering and extracting insightful information from digital conversations, namely those human?human information exchanges mediated by digital network technology. We present in this article a computational model of natural arguments and its implementation for the automatic argumentative analysis of digital conversations, which allows us to produce relevant information to build interaction business analytics applications overcoming the limitations of standard text mining and information retrieval technology. Applications include advanced visualisations and abstractive summaries.

Whereas there are many publications in which argumentation quality has been defined by argumentation theorists, considerably less research attention has been paid to lay people’s considerations regarding argument quality. Considerations about strong and weak argumentation are relevant because they can be compared with actual persuasive success. Argumentation theorists’ conceptions have to some extent been shown to be compatible with actual effectiveness, but for lay people such compatibility has yet to be determined. This study experimentally investigated lay people’s expectations about the (...) persuasiveness of anecdotal, statistical, causal, and expert evidence, and compared these expectations with the actual persuasiveness of these evidence types. Dutch and French participants (N = 174) ranked four types of evidence in terms of their expected persuasiveness for eight different claims. Both cultural groups expected statistical evidence to be the most persuasive type of evidence to other people, followed by expert, causal, and, finally, anecdotal evidence. A comparison of these rankings with the results of Hornikx and Hoeken (Communication Monographs 74, 443–463, 2007, Study 1) on the actual persuasiveness of the same evidence types reveals that people’s expectations are generally accurate: How relatively persuasive they expect evidence types to be often corresponded with their actual persuasiveness. (shrink)