This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning (...) information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning. (shrink)

This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...) how elements of accepted theory constrain emergent models and plausible inferences to explanations--in a quasi-rationalist fashion. I draw on illustrations from quantum gravity below just because there is so little direct evidence available in the field. It is in such cases that Peirce's discussions of abductive inference provide the most plausible support for the idea of a logic of abduction--as inference to readily formulatable explanatory hypotheses. The possible need for conceptual innovation points to the limits on the possibility of a logic of abduction of a more rationalistic character--selecting uniquely superior explanations. Abduction conceived as a repeated cycle of inquiry also points to limits on our expectations for an abductive logic. My chief point is that the character of inference to an explanation, viewed below as embedded within arguments from analogy, is so little compelling, as a matter of logical form alone, that there will always be a pluralism of plausible alternatives among untested hypotheses and inferences to them--calling for some comparative evaluation. This point leads on to some consideration of the virtues of hypotheses--as a description of the range of this pluralism. (shrink)

Humans continuously delegate and distribute cognitive functions to the environment to lessen their limits. They build models, representations, and other various mediating structures, that are thought to be good to think. The case of scientific innovation is particularly important: the main aim of this paper is to revise and criticize the concept of scientific innovation, reframing it in what I will call an eco-epistemic perspective, taking advantage of recent results coming from the area of distributed cognition (common coding) and abductive (...) cognition (manipulative). Taking advantage of this eco-cognitive perspective the article outlines how innovative scientific modeling activity can be better described taking advantage of the concept of “epistemic warfare”, which sees scientific enterprise as a complicated struggle for rational knowledge in which it is crucial to distinguish epistemic (for example scientific models) from non epistemic (for example fictions, falsities, propaganda) weapons. (shrink)

The roles of abductive inference in dynamic heuristics allows scientific methodologies to test novel explanations for the world’s ways. Deliberate reasoning often follows abductive patterns, as well as patterns dominated by deduction and induction, but complex mixtures of these three modes of inference are crucial for scientific explanation. All possible mixed inferences are formulated and categorized using a novel typology and nomenclature. Twenty five possible combinations among abduction, induction, and deduction are assembled and analyzed in order of complexity. There are (...) five primary categories for sorting these inferential procedures: fallacies, non-scientific procedures, quasi-scientific procedures, scientific procedures, and scientific heuristics. (shrink)