Looking in the Wrong (La)place? The Promise and Perils of Becoming Big Data

Bibliographic Data

ID5337017
AuthorsLawrence Busch (Michigan State University, corresponding author)
Year2017
Volume42
Issue4
Pages657-678
Publication date2017-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueScience Technology & Human Values (JOURNAL)
Journal identifiersISSN: 0162-2439 • E-ISSN: 1552-8251
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/0162243916677835
OpenAlexW2554227621
LanguageEN
Citations received12
References cited30

Laplace once argued that if one could "comprehend all the forces by which nature is animated," it would be possible to predict the future and explain the past. The advent of analysis of large-scale data sets has been accompanied by newfound concerns about "Laplace's Demon" as it relates to certain fields of science as well as management, evaluation, and audit. I begin by asking how statistical data are constructed, illustrating the hermeneutic acts necessary to create a variable. These include attributing a certain characteristic to a particular phenomenon, isolating the characteristic of interest, and assigning a value to it. In addition, a population must be identified and a sample must be "taken" from that population. Next, I examine how statistical analyses are conducted, examining the interpretive acts there as well. In each case, I show how big data add new challenges. I then show how statistics are incorporated into audits and evaluations, emphasizing how alternative interpretations are concealed in the audit process. I conclude by noting that these issues cannot be "resolved" as Laplace suggested. His Demon, already banished from physics, needs to be banished from other fields of science, management, audits, and evaluations as well

Audit · Big data · Cartography · Data mining · Data science · Demon · Economics · Epistemology · Geography · Management · Phenomenon · Physics · Population · Sociology · Statistics · Complex Systems and Time Series Analysis · Computer Science · Data Analysis with R · Data-Driven Disease Surveillance · Mathematics · Philosophy · Psychology

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