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Scientific discovery, causal explanation, and process model induction

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  • Pat Langley

    (Institute for the Study of Learning and Expertise)

Abstract

In this paper, I review two related lines of computational research: discovery of scientific knowledge and causal models of scientific phenomena. I also report research on quantitative process models that falls at the intersection of these two themes. This framework represents models as a set of interacting processes, each with associated differential equations that express influences among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes enables search through the space of model structures and associated parameters to find explanations of time-series data. I discuss the representation of such process models, their use for prediction and explanation, and their discovery through heuristic search, along with their interpretation as causal accounts of dynamic behavior.

Suggested Citation

  • Pat Langley, 2019. "Scientific discovery, causal explanation, and process model induction," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 18(1), pages 43-56, June.
    • Handle: RePEc:spr:minsoc:v:18:y:2019:i:1:d:10.1007_s11299-019-00216-1
    DOI: 10.1007/s11299-019-00216-1
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    References listed on IDEAS

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    1. Ross D. King & Kenneth E. Whelan & Ffion M. Jones & Philip G. K. Reiser & Christopher H. Bryant & Stephen H. Muggleton & Douglas B. Kell & Stephen G. Oliver, 2004. "Functional genomic hypothesis generation and experimentation by a robot scientist," Nature, Nature, vol. 427(6971), pages 247-252, January.
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