Scientific Discovery in a Space of Structural Models: An Example from the History of Solution Chemistry

Adrian Gordon, Peter Edwards, Derek Sleeman, Yves Kodratoff

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Abstract

Much previous work in developing computational models of scientific discovery has concentrated on the formation of basic laws. The important role played by additional assumptions in this process is a neglected research topic. We argue that hypotheses about structure are an important source of such additional assumptions, and that knowledge of this type can be embodied in the notion of Informal Qualitative Models (IQMs). In this paper, we demonstrate that such models can be synthesised by applying a set of operators to the most fundamental model in a domain. Heuristics are employed to control this process, which forms the basis of an architecture for model-driven scientific discovery. Conventional data-driven discovery techniques can be integrated into this architecture, resulting in laws which depend crucially on the model that is applied to a problem. This approach is illustrated by an historical survey of eighteenth and nineteenth century solution chemistry, which focuses on the evolution of the models employed by scientists. A series of models are synthesised which reflect these historical developments, showing the importance of structural models both in understanding certain aspects of the scientific discovery process, and as a basis for practical discovery systems.
Original languageEnglish
Title of host publicationProceedings of the Sixteenth Annual Conference of the Cognitive Science Society
PublisherLawrence Erlbaum Associates
Pages381-386
Publication statusPublished - 1994

Keywords

  • History of Science
  • scientific discovery
  • computational model

Cite this

Gordon, A., Edwards, P., Sleeman, D., & Kodratoff, Y. (1994). Scientific Discovery in a Space of Structural Models: An Example from the History of Solution Chemistry. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 381-386). Lawrence Erlbaum Associates. https://mindmodeling.org/cogscihistorical/cogsci_16.pdf