Model-Based Inferences in Modeling of Complex Systems
| Autor: | Miles Alexander James MacLeod |
|---|---|
| Přispěvatelé: | Philosophy |
| Rok vydání: | 2018 |
| Předmět: |
Philosophy of science
Computer science Modelling biological systems media_common.quotation_subject 05 social sciences UT-Hybrid-D Complex system Inference 050905 science studies Data science 050105 experimental psychology Field (computer science) Philosophy Debugging 0501 psychology and cognitive sciences 0509 other social sciences Centrality Philosophy of technology media_common |
| Zdroj: | Topoi, 39, 915-925. Springer |
| ISSN: | 1572-8749 0167-7411 |
| DOI: | 10.1007/s11245-018-9569-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink