Aggregating multilevel mechanistic models from Big Data with Machine Learning. 2018. abstract bibtex The nature of scientific evidence and its logical and conceptual relations with hypotheses, theories, and models have been among the most enticing topics in the philosophy of science. Philosophers and scientists alike were always toiling to find new methodologies to relate better scientific evidence to theories, models, and hypotheses. Scientific revolutions, as well as everyday progress of science, depend on developing methods to deal with an increase in complexity of the information science processes: climate change, political and economic instability, devastating natural disasters, new challenges coming from emerging technologies, new data from particle accelerators, telescopes, the vast data from various ‘-omics’ disciplines, and ultimately the whole Internet as a vast source of information. The advancement of computational tools and the presence of massive amounts of data, called Big Data, became arguably part of the practice of some scientific disciplines in the three decades. Presumably, philosophers of science should pay more attention to the epistemology of data mining in these new contexts finds its natural place in the already convoluted problem of scientific evidence. We ask this question from an epistemological point of view: what is (a) specific to and (b) novel about how data-driven, and computational-intensive scientific disciplines build their models?
@unpublished{AggregatingMultilevelMechanistic2018,
title = {Aggregating multilevel mechanistic models from {Big} {Data} with {Machine} {Learning}},
copyright = {All rights reserved},
abstract = {The nature of scientific evidence and its logical and conceptual relations with hypotheses, theories, and models have been among the most enticing topics in the philosophy of science. Philosophers and scientists alike were always toiling to find new methodologies to relate better scientific evidence to theories, models, and hypotheses. Scientific revolutions, as well as everyday progress of science, depend on developing methods to deal with an increase in complexity of the information science processes: climate change, political and economic instability, devastating natural disasters, new challenges coming from emerging technologies, new data from particle accelerators, telescopes, the vast data from various ‘-omics’ disciplines, and ultimately the whole Internet as a vast source of information. The advancement of computational tools and the presence of massive amounts of data, called Big Data, became arguably part of the practice of some scientific disciplines in the three decades. Presumably, philosophers of science should pay more attention to the epistemology of data mining in these new contexts finds its natural place in the already convoluted problem of scientific evidence. We ask this question from an epistemological point of view: what is (a) specific to and (b) novel about how data-driven, and computational-intensive scientific disciplines build their models?},
language = {1. Philosophy of science},
year = {2018},
keywords = {Big Data, Mechanisms, Patterns},
}
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