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Nersessian, N. J. (1999). Model-based reasoning in conceptual change. In Model-based reasoning in scientific discovery (pp. 5–22).Springer. 
Added by: SijanLibrarian (2022-06-14 13:38:29)   Last edited by: SijanLibrarian (2022-06-14 13:41:07)
Resource type: Book Article
BibTeX citation key: Nersessian1999
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Categories: Cognitive Science, Complexity Science, Data Sciences, Decision Theory, Engineering, General, Mathematics, Philosophy
Subcategories: Analytics, Big data, Decision making, Human decisionmaking, Human factors engineering, Informatics, Systems theory
Creators: Nersessian
Publisher: Springer
Collection: Model-based reasoning in scientific discovery
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This paper addresses how specific modeling practices employed by scientists are productive methods of conceptual change in science. Within philosophy, where the identification of reasoning with argument and logic is deeply ingrained, these practices have not traditionally been considered significant forms of scientific reasoning. Embracing these modeling practices as “methods” of conceptual change in science requires expanding philosophical notions of scientific reasoning to encompass forms of creative reasoning. I focus on three forms of model-based reasoning demonstrated in my previous work as generative of conceptual change in science: analogical modeling, visual modeling, and thought experimenting. The models are intended as interpretations 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. In the modeling process, various forms of abstraction, such as limiting case, idealization, generalization, generic modeling, are utilized. Evaluation and adaptation take place in light of structural, causal, and/or functional constraint satisfaction. Simulation can be used to produce new states and enable evaluation of behaviors, constraint satisfaction, and other factors.
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