Big Nonsense: the end of scientific thinking is near…

Astronomers of the Maya civilization and astronomers of the Babylonian civilization were brilliant in predicting astronomical events. For instance, from meticulous observations of the Sun, Moon, Venus and Jupiter they were able to predict with astonishing accuracy the 584-day cycle of Venus or the details of the celestial track of Jupiter [1]. Yet they had no clue about our heliocentric solar system, they believed that the earth was flat and they were completely ignorant of the real movement of stars and planets while being convinced that the sky was supported by four jaguars, each holding up a corner of the sky.

They were basically doing what is now called Big Data or Data Science, a very powerful way to uncover patterns in historical data. Unfortunately, data science on its own might introduce false interpretations of causality, like jaguars carrying the sky. What we need in addition to that are computational predictive models that use fundamental first principles and mechanisms that can track the system over time and allow for quantitative validation and provide pointers to novel experiments to falsify or confirm our interpretations. In other words, we need a merger of the 'inductivism' from Sir Francis Bacon (Big Data) and the 'deductivism' from Sir Carl Popper (first principle computational models) [2,3].

If we manage to turn a potential clash of these two titans of science into an integrated scientific paradigm, then the holy grail of the Scientific Method as a way 'to discover that Nature hasn’t misled you into thinking you know something you don’t actually know ' [4] will be one step closer.

See also a lecture I presented in May 2017 at the Complexity Hub in Vienna: Here.

[1] M. Ossendrijver, Science: Vol. 351, Issue 6272, pp. 482-484. (2016)
[2] P.M.A. Sloot, P. Coveney and J. Dongarra: Journal of Computational Science Vol. 1 (2010) 3–4;
[3] P.M.A. Sloot in 43 Visions for Complexity, Ed. S. Thurner, p65-66 World Scientific Publishing Co. Pte. Ltd. ISBN 978-981-3206-84-7, 2017
[4] Robert M. Pirsig, 'Zen in the art of motorcycle maintenance', 1974