On John Allen's critique of induction

Kelley, Lawrence A.; Scott, Michael

doi:10.1002/bies.1124

Cited by 8 publications

(5 citation statements)

References 1 publication

(3 reference statements)

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“…There certainly are some successes with machine learning, such as text parsing, image and handwriting recognition, spam filters, and credit card fraud detection. The apparent inductive nature of these has certainly given rise to debate (e.g., Allen, 2001a, 2001b; Gillies, ; Kell & Oliver, ; Kelley & Scott, ).…”

Section: The Curve‐fitting Problem Machine Learning and Statisticalmentioning

confidence: 99%

Big data and its epistemology

Frické

2014

Asso for Info Science & Tech

101

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The article considers whether Big Data, in the form of data‐driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now‐discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data‐driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling (“unsound statistical fiddling”). The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data‐driven science is a chimera.

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Section: The Curve‐fitting Problem Machine Learning and Statisticalmentioning

confidence: 99%

Big data and its epistemology

Frické

2014

Asso for Info Science & Tech

101

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“…A discussion of some of these important issues (4) was initiated in this journal by John Allen, (5) and elicited some further debate. (6)(7)(8)(9)(10) However, the somewhat polemical starting position (5) inevitably organised the combatants into an either-or view that is too simplistic. The purpose of this essay is to promote the view that the hypothesis-driven and inductive modes of reasoning are not competitive but complementary (see also Ref.…”

Section: Introductionmentioning

confidence: 99%

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the post‐genomic era

2003

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It is considered in some quarters that hypothesis-driven methods are the only valuable, reliable or significant means of scientific advance. Data-driven or 'inductive' advances in scientific knowledge are then seen as marginal, irrelevant, insecure or wrong-headed, while the development of technology--which is not of itself 'hypothesis-led' (beyond the recognition that such tools might be of value)--must be seen as equally irrelevant to the hypothetico-deductive scientific agenda. We argue here that data- and technology-driven programmes are not alternatives to hypothesis-led studies in scientific knowledge discovery but are complementary and iterative partners with them. Many fields are data-rich but hypothesis-poor. Here, computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post-genomic era.

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“…Supporters of Big Data do not disprove the idea that even the computational approach involves the testing of certain assumptions, for example some search algorithm which is included in the data analysis program. But these assumptions do not provide an explanation of the phenomenon involved, merely defining strategies to identify relationships between sets of data .…”

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confidence: 99%