Model-Based Inferences in Modeling of Complex Systems

Miles Alexander James MacLeod (Corresponding Author)

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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
JournalTopoi
DOIs
Publication statusAccepted/In press - 21 Jun 2018

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Inference
Complex Systems
Modeling
Computational
Philosophy of Science
Scientific Practice
Debugging
Incomplete
Scientific Discovery
Centrality
Systems Biology
Problem Solving

Keywords

  • UT-Hybrid-D

Cite this

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Model-Based Inferences in Modeling of Complex Systems. / MacLeod, Miles Alexander James (Corresponding Author).

In: Topoi, 21.06.2018.

Research output: Contribution to journalArticleAcademicpeer-review

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