Abstract
For it is not the abundance of knowledge, but the interior feeling and taste of things, which is accustomed to satisfy the desire of the soul. (Saint Ignatius of Loyola).We argue that the boldest claims of big data (BD) are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of BD are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, non-locality and hyperdimensions which one encounters frequently in multi-scale modelling of complex systems.This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.
Highlights
Our current society is characterized by an unprecedented ability to produce and store breathtaking amounts of data and, much more importantly, by the ability to navigate across them in such a way as to distil from them useful information, knowledge
big data (BD) flourishes upon four main observations, namely (i) The explosive growth of data production/acquisition/navigation capabilities. (ii) Reading off patterns from complex datasets through smart search algorithms may be faster and more revealing than modelling the underlying behaviour, i.e. using theory. (iii) It applies to any discipline, including those traditionally not deemed suitable for mathematical treatment, including life sciences. (iv) Its involvement in immediate application to business and politics, ‘opinion dynamics’, ‘sentiment analysis’ and so on, furnishes another set of domains which raise many ethical questions
Infinite variance is a bit far-fetched, since in the real-world signals and measurements are necessarily finite, but the message comes across loud and clear: the mean and the variance are no longer sufficient to capture the statistical nature of the phenomenon
Summary
Our current society is characterized by an unprecedented ability to produce and store breathtaking amounts of data and, much more importantly, by the ability to navigate across them in such a way as to distil from them useful information, knowledge. This extreme stance is summarized in Anderson’s provocative statement: ‘With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories’. In a nutshell, it is a data-driven version of Archimedes’ fulcrum: give me enough data and I shall move the world. It is a data-driven version of Archimedes’ fulcrum: give me enough data and I shall move the world As radical as this new empiricism is, it brings up an intriguing point: is understanding overrated? We shall offer rational arguments in support of this instinctive reaction whilst recognizing the perspectives opened up by BD approaches
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