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Big Data – The New Science of Complexity

Pietsch, Wolfgang (2013) Big Data – The New Science of Complexity. In: UNSPECIFIED.

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Abstract

Data-intensive techniques, now widely referred to as 'big data', allow for novel ways to address complexity in science. I assess their impact on the scientific method. First, big-data science is distinguished from other scientific uses of information technologies, in particular from computer simulations. Then, I sketch the complex and contextual nature of the laws established by data-intensive methods and relate them to a specific concept of causality, thereby dispelling the popular myth that big data is only concerned with correlations. The modeling in data-intensive science is characterized as 'horizontal'—lacking the hierarchical, nested structure familiar from more conventional approaches. The significance of the transition from hierarchical to horizontal modeling is underlined by a concurrent paradigm shift in statistics from parametric to non-parametric methods.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Pietsch, Wolfgangpietsch@cvl-a.tum.de
Keywords: big data, data-intensive science, computer simulations, modeling, complexity, non-parametric statistics, causation
Subjects: General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Computer Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Explanation
General Issues > Laws of Nature
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
General Issues > Reductionism/Holism
General Issues > Structure of Theories
General Issues > Technology
Depositing User: Wolfgang Pietsch
Date Deposited: 23 Aug 2013 13:48
Last Modified: 23 Aug 2013 13:48
Item ID: 9944
Subjects: General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Computer Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Explanation
General Issues > Laws of Nature
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
General Issues > Reductionism/Holism
General Issues > Structure of Theories
General Issues > Technology
Date: 23 August 2013
URI: http://philsci-archive.pitt.edu/id/eprint/9944

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