 On Building Better Mousetraps and Understanding the Human ConditionJimmy Lin The ANNALS of the American Academy of Political and Social Science, 2015, vol. 659, issue 1, 33-47 Abstract: Over the past few years, we have seen the emergence of “big data†: disruptive technologies that have transformed commerce, science, and many aspects of society. Despite the tremendous enthusiasm for big data, there is no shortage of detractors. This article argues that many criticisms stem from a fundamental confusion over goals: whether the desired outcome of big data use is “better science†or “better engineering.†Critics point to the rejection of traditional data collection and analysis methods, confusion between correlation and causation, and an indifference to models with explanatory power. From the perspective of advancing social science, these are valid reservations. I contend, however, that if the end goal of big data use is to engineer computational artifacts that are more effective according to well-defined metrics, then whatever improves those metrics should be exploited without prejudice. Sound scientific reasoning, while helpful, is not necessary to improve engineering. Understanding the distinction between science and engineering resolves many of the apparent controversies surrounding big data and helps to clarify the criteria by which contributions should be assessed. Keywords: big data; computational social science; machine learning; data mining; log analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:anname:v:659:y:2015:i:1:p:33-47 Access Statistics for this article More articles in The ANNALS of the American Academy of Political and Social Science |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.