Abstract
In this chapter, we review research on computational approaches to scientific discovery, starting with early work on the induction of numeric laws before turning to the construction of models that explain observations in terms of domain knowledge. We focus especially on inductive process modeling, which involves finding a set of linked differential equations, organized into processes, that reproduce, predict, and explain multivariate time series. We review the notion of quantitative process models, present two approaches to their construction that search through a space of model structures and associated parameters, and report their successful application to the explanation of ecological data. After this, we explore the relevance of process models to the social sciences, including the reasons they seem appropriate and some challenges to discovering them. In closing, we discuss other causal frameworks, including structural equation models and agent-based accounts, that researchers have developed to construct models of social phenomena.
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Notes
- 1.
Some variants (e.g., Bridewell & Langley 2010) ensure that candidate structures are consistent with constraints on relations among processes, say that an organism cannot take part in two distinct growth elements. These offer another form of theoretical knowledge about the domain.
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Acknowledgements
The research reported in this chapter was supported by Grant No. N00014-11-1-0107 from the US Office of Naval Research, which is not responsible for its contents. We thank Will Bridewell, Sašo Džeroski, Ruolin Jia, and Ljupčo Todorovski for useful discussions that led to the results reported.
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Langley, P., Arvay, A. (2019). Scientific Discovery, Process Models, and the Social Sciences. In: Addis, M., Lane, P.C.R., Sozou, P.D., Gobet, F. (eds) Scientific Discovery in the Social Sciences. Synthese Library, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-23769-1_11
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