Model-Based Inferences in Modeling of Complex Systems

Miles Alexander James MacLeod*

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    2 Citations (Scopus)
    154 Downloads (Pure)

    Abstract

    Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based reasoning in day-to-day problem-solving practices, and the role that debugging processes of partial or incomplete models can play in scientific inference and scientific discovery, particularly with respect to complex systems. We present several deeper implications such an analysis has for philosophy of science regarding the role of models in scientific practice.
    Original languageEnglish
    Pages (from-to)915-925
    JournalTopoi
    Volume39
    DOIs
    Publication statusPublished - 1 Sept 2020

    Keywords

    • UT-Hybrid-D

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