The Fate of Explanatory Reasoning in the Age of Big Data
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The Fate of Explanatory Reasoning in the Age of Big Data Frank Cabrera 1 Received: 19 November 2019 / Accepted: 4 August 2020/ # Springer Nature B.V. 2020
Abstract In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are in tension with a prominent account of scientific method, often called “Inference to the Best Explanation” (IBE). Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, (2013) that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368, (2017). In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement” (CCR), which states, roughly, that causalnomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. Keywords Inference to the best explanation . Bayesianism . Scientific inference . Cicero .
Big data . Data-intensive science . Data-driven science
* Frank Cabrera [email protected]
1
Milwaukee School of Engineering, Grohmann Museum 202, 1000 N Broadway, Milwaukee, WI 53202, USA
F. Cabrera
1 Introduction In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science which bear on scientific methodology. According to these “Big Data” enthusiasts, as our ability to gather and analyze data increases, the nature of scientific practice will change dramatically. In the future, theorizing will to a significant degree become obsolete. As a result, future science will be data-driven, rather than hypothesis-driven. Moreover, scientists will be able to dispense with theoretical background assumptions, and in particular scientists will no longer have any use for familiar concepts like causation and explanation. Given that these methodological claims seem to fly in the face of current scientific practice, all of them are worthy of p
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