A bottom-up ‘Better Best System ’ of lawhood: compressibility , patterns, and mining Big Data. October 2017. abstract bibtex This paper starts with an enduring empiricist concern about laws of nature : how do we relate laws of nature to empirical data? Although this issue has been raised in Early modern philosophy (Hume, Reid) this paper looks at this perennial issue from the perspective of the way we collect and process data in the 21st century, when many disciplines (astronomy, genetics, neuroscience, climate science, particle physics, social sciences, etc.) have shifted towards data-oriented and computational-intensive scientific practice. Is there a future for laws of nature in the data-oriented science? If so, which account of lawhood fares better in this sense? To address this question, this paper pursues two goals. First, it discusses the lawhood of powerful generalizations in scientific disciplines dominated by Big Data, computational power, and numerical methods. Contrary to a common hype, it is argued here that laws of nature will have a role to play in the advancement of science after the so-called “fourth-paradigm” stage of science. Relying on data alone (be it Big Data, or small data) is not science: for epistemological reasons, it is argued here, we need laws of nature (and as others may argue: theories, models, causation, mechanisms) to advance in the 21st century: nevertheless, some of these concepts need to be refitted to match the challenges emerging after the 21st century “data turn.” The second part of the argument asks: which account of lawhood is suitable for the stage of data-driven, computational-intensive science? What means to be a law of nature when Big Data counts as your scientific evidence and computational tools (including numerical simulations, machine learning, discovery algorithms, data mining) are part and parcel of the scientific method? Most of the philosophical accounts of lawhood: deflationary views, governing views, and various ‘systems’ views: Mill-Ramsey-Lewis or Cohen’s and C. Callender’s ‘better best system’ (BBS) need to be amended to accommodate this type of empiricism about laws of nature. The version of the BBS proposed here can account for scenarios in which laws of nature are distilled from data through various computational techniques. This paper emphasizes that Big Data is not restricted to one discipline or to one representation and that the set of natural kinds that Big Data comes with transgresses our own aims and ways of dividing science. In the ‘bottom-up’ BBS discussed here, a prospective ‘correspondence’ between Big Data and the laws of nature is proffered: data is associated with the Humean mosaic, and hence laws are supervening on data, not directly on occurrent facts; patterns in data are related to generalizations and ‘natural’ patterns to laws of nature; natural kinds are not restricted to one scientific discipline, but emerge from data. Laws of nature are then defined by structures in large datasets and computational methods used to mining Big Data and distill laws from mere generalizations. We focus in the concept of ‘natural pattern’ and relate it to the existing philosophical discussions: ‘small’ (Floridi 2012, 2011), ‘real,’ or ‘autonomous’ patterns. (Bogen 2010; Dennett 1991; Brading 2010) A couple of examples of bottom up lawhood inspired from recent literature are quickly discussed.
@unpublished{BottomupBetterBest2017a,
title = {A bottom-up ‘{Better} {Best} {System} ’ of lawhood: compressibility , patterns, and mining {Big} {Data}},
copyright = {All rights reserved},
abstract = {This paper starts with an enduring empiricist concern about laws of nature : how do we relate laws of nature to empirical data? Although this issue has been raised in Early modern philosophy (Hume, Reid) this paper looks at this perennial issue from the perspective of the way we collect and process data in the 21st century, when many disciplines (astronomy, genetics, neuroscience, climate science, particle physics, social sciences, etc.) have shifted towards data-oriented and computational-intensive scientific practice. Is there a future for laws of nature in the data-oriented science?
If so, which account of lawhood fares better in this sense?
To address this question, this paper pursues two goals. First, it discusses the lawhood of powerful generalizations in scientific disciplines dominated by Big Data, computational power, and numerical methods. Contrary to a common hype, it is argued here that laws of nature will have a role to play in the advancement of science after the so-called “fourth-paradigm” stage of science. Relying on data alone (be it Big Data, or small data) is not science: for epistemological reasons, it is argued here, we need laws of nature (and as others may argue: theories, models, causation, mechanisms) to advance in the 21st century: nevertheless, some of these concepts need to be refitted to match the challenges emerging after the 21st century “data turn.”
The second part of the argument asks: which account of lawhood is suitable for the stage of data-driven, computational-intensive science? What means to be a law of nature when Big Data counts as your scientific evidence and computational tools (including numerical simulations, machine learning, discovery algorithms, data mining) are part and parcel of the scientific method? Most of the philosophical accounts of lawhood: deflationary views, governing views, and various ‘systems’ views: Mill-Ramsey-Lewis or Cohen’s and C. Callender’s ‘better best system’ (BBS) need to be amended to accommodate this type of empiricism about laws of nature. The version of the BBS proposed here can account for scenarios in which laws of nature are distilled from data through various computational techniques. This paper emphasizes that Big Data is not restricted to one discipline or to one representation and that the set of natural kinds that Big Data comes with transgresses our own aims and ways of dividing science. In the ‘bottom-up’ BBS discussed here, a prospective ‘correspondence’ between Big Data and the laws of nature is proffered: data is associated with the Humean mosaic, and hence laws are supervening on data, not directly on occurrent facts; patterns in data are related to generalizations and ‘natural’ patterns to laws of nature; natural kinds are not restricted to one scientific discipline, but emerge from data. Laws of nature are then defined by structures in large datasets and computational methods used to mining Big Data and distill laws from mere generalizations. We focus in the concept of ‘natural pattern’ and relate it to the existing philosophical discussions: ‘small’ (Floridi 2012, 2011), ‘real,’ or ‘autonomous’ patterns. (Bogen 2010; Dennett 1991; Brading 2010) A couple of examples of bottom up lawhood inspired from recent literature are quickly discussed.},
month = oct,
year = {2017},
keywords = {Big Data, Humean, Laws of Nature, Patterns},
}
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Although this issue has been raised in Early modern philosophy (Hume, Reid) this paper looks at this perennial issue from the perspective of the way we collect and process data in the 21st century, when many disciplines (astronomy, genetics, neuroscience, climate science, particle physics, social sciences, etc.) have shifted towards data-oriented and computational-intensive scientific practice. Is there a future for laws of nature in the data-oriented science? If so, which account of lawhood fares better in this sense? To address this question, this paper pursues two goals. First, it discusses the lawhood of powerful generalizations in scientific disciplines dominated by Big Data, computational power, and numerical methods. Contrary to a common hype, it is argued here that laws of nature will have a role to play in the advancement of science after the so-called “fourth-paradigm” stage of science. Relying on data alone (be it Big Data, or small data) is not science: for epistemological reasons, it is argued here, we need laws of nature (and as others may argue: theories, models, causation, mechanisms) to advance in the 21st century: nevertheless, some of these concepts need to be refitted to match the challenges emerging after the 21st century “data turn.” The second part of the argument asks: which account of lawhood is suitable for the stage of data-driven, computational-intensive science? What means to be a law of nature when Big Data counts as your scientific evidence and computational tools (including numerical simulations, machine learning, discovery algorithms, data mining) are part and parcel of the scientific method? Most of the philosophical accounts of lawhood: deflationary views, governing views, and various ‘systems’ views: Mill-Ramsey-Lewis or Cohen’s and C. Callender’s ‘better best system’ (BBS) need to be amended to accommodate this type of empiricism about laws of nature. The version of the BBS proposed here can account for scenarios in which laws of nature are distilled from data through various computational techniques. This paper emphasizes that Big Data is not restricted to one discipline or to one representation and that the set of natural kinds that Big Data comes with transgresses our own aims and ways of dividing science. In the ‘bottom-up’ BBS discussed here, a prospective ‘correspondence’ between Big Data and the laws of nature is proffered: data is associated with the Humean mosaic, and hence laws are supervening on data, not directly on occurrent facts; patterns in data are related to generalizations and ‘natural’ patterns to laws of nature; natural kinds are not restricted to one scientific discipline, but emerge from data. Laws of nature are then defined by structures in large datasets and computational methods used to mining Big Data and distill laws from mere generalizations. We focus in the concept of ‘natural pattern’ and relate it to the existing philosophical discussions: ‘small’ (Floridi 2012, 2011), ‘real,’ or ‘autonomous’ patterns. (Bogen 2010; Dennett 1991; Brading 2010) A couple of examples of bottom up lawhood inspired from recent literature are quickly discussed.","month":"October","year":"2017","keywords":"Big Data, Humean, Laws of Nature, Patterns","bibtex":"@unpublished{BottomupBetterBest2017a,\n\ttitle = {A bottom-up ‘{Better} {Best} {System} ’ of lawhood: compressibility , patterns, and mining {Big} {Data}},\n\tcopyright = {All rights reserved},\n\tabstract = {This paper starts with an enduring empiricist concern about laws of nature : how do we relate laws of nature to empirical data? Although this issue has been raised in Early modern philosophy (Hume, Reid) this paper looks at this perennial issue from the perspective of the way we collect and process data in the 21st century, when many disciplines (astronomy, genetics, neuroscience, climate science, particle physics, social sciences, etc.) have shifted towards data-oriented and computational-intensive scientific practice. Is there a future for laws of nature in the data-oriented science?\n\nIf so, which account of lawhood fares better in this sense?\n\nTo address this question, this paper pursues two goals. First, it discusses the lawhood of powerful generalizations in scientific disciplines dominated by Big Data, computational power, and numerical methods. Contrary to a common hype, it is argued here that laws of nature will have a role to play in the advancement of science after the so-called “fourth-paradigm” stage of science. Relying on data alone (be it Big Data, or small data) is not science: for epistemological reasons, it is argued here, we need laws of nature (and as others may argue: theories, models, causation, mechanisms) to advance in the 21st century: nevertheless, some of these concepts need to be refitted to match the challenges emerging after the 21st century “data turn.”\n\nThe second part of the argument asks: which account of lawhood is suitable for the stage of data-driven, computational-intensive science? What means to be a law of nature when Big Data counts as your scientific evidence and computational tools (including numerical simulations, machine learning, discovery algorithms, data mining) are part and parcel of the scientific method? Most of the philosophical accounts of lawhood: deflationary views, governing views, and various ‘systems’ views: Mill-Ramsey-Lewis or Cohen’s and C. Callender’s ‘better best system’ (BBS) need to be amended to accommodate this type of empiricism about laws of nature. The version of the BBS proposed here can account for scenarios in which laws of nature are distilled from data through various computational techniques. This paper emphasizes that Big Data is not restricted to one discipline or to one representation and that the set of natural kinds that Big Data comes with transgresses our own aims and ways of dividing science. In the ‘bottom-up’ BBS discussed here, a prospective ‘correspondence’ between Big Data and the laws of nature is proffered: data is associated with the Humean mosaic, and hence laws are supervening on data, not directly on occurrent facts; patterns in data are related to generalizations and ‘natural’ patterns to laws of nature; natural kinds are not restricted to one scientific discipline, but emerge from data. 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