Contemporary science confronts a huge wall: lack of data. Scientists desire to model large-scale systems that are both stochastic and nonlinear, for instance cell regulation; however, relative to the complexity of such systems there is a paucity of data that impedes our ability to both construct and validate models. With regard to large-scale systems and lack of data, this chapter has several aims: (1) discuss the relationship between scientific epistemology grounded in prediction and data insufficiency; (2) discuss the dichotomy between pure science, whose aim is to provide a mathematical representation of Nature, and translational science (engineering), whose aim is the pragmatic application of mathematical modeling to beneficially intervene in Nature, and the manner in which translational science mitigates the data requirement; (3) provide a detailed analysis of the effect of limited data as it applies to pattern recognition; and (4) provide a framework for translational science in the context of model uncertainty, including an epistemologically consequent concept of uncertainty quantification.
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- Scientific Epistemology in the Context of Uncertainty
Edward R. Dougherty
- Chapter 6
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