Model-Based Reasoning in Scientific Discovery

  • Lorenzo Magnani
  • Nancy J. Nersessian
  • Paul Thagard

Table of contents

  1. Front Matter
    Pages i-1
  2. Models, Mental models, and Representations

    1. Front Matter
      Pages 3-3
    2. Nancy J. Nersessian
      Pages 5-22
    3. Ronald N. Giere
      Pages 41-57
    4. Kenneth J. Knoespel
      Pages 59-73
    5. Mauricio Suárez
      Pages 75-83
  3. Discovery Processes and Mechanisms

  4. Creative Inferences and Abduction

    1. Front Matter
      Pages 197-197
    2. Lorenzo Magnani
      Pages 219-238
    3. Eric Winsberg
      Pages 255-269
    4. Vincent F. Hendricks, Jan Faye
      Pages 271-292
  5. Back Matter
    Pages 327-343

About this book


The volume is based on the papers that were presented at the Interna­ tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea­ soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi­ tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de­ ductive and inductive arguments. Understanding the contribution of model­ ing practices to discovery and conceptual change in science requires ex­ panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso­ phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model­ based reasoning to be considered in this book. The models are intended as in­ terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.


Action Bayesian inference Cognitive science Constraint Satisfaction Philosophy of Science artificial intelligence complex system complexity logic model modeling programming programming language scientific discovery simulation

Editors and affiliations

  • Lorenzo Magnani
    • 1
  • Nancy J. Nersessian
    • 2
  • Paul Thagard
    • 3
  1. 1.University of PaviaPaviaItaly
  2. 2.Georgia Institute of TechnologyAtlantaUSA
  3. 3.University of WaterlooWaterlooCanada

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic / Plenum Publishers, New York 1999
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7181-6
  • Online ISBN 978-1-4615-4813-3
  • Buy this book on publisher's site