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Ad Hoc Hypotheses and the Monsters within

Votsis, Ioannis (2016) Ad Hoc Hypotheses and the Monsters within. Fundamental Issues of Artificial Intelligence. pp. 299-313.

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Abstract

Science is increasingly becoming automated. Tasks yet to be fully
automated include the conjecturing, modifying, extending and testing of hypotheses. At present scientists have an array of methods to help them carry out those tasks. These range from the well-articulated, formal and unexceptional rules to the semi-articulated and variously understood rules-of-thumb and intuitive hunches. If we are to hand over at least some of the aforementioned tasks to machines, we need to
clarify, refine and make formal, not to mention computable, even the more obscure of the methods scientists successfully employ in their inquiries. The focus of this essay is one such less-than-transparent methodological rule. I am here referring to the rule that ad hoc hypotheses ought to be spurned. This essay begins with a brief examination of some notable conceptions of ad hoc-ness in the philosophical literature. It is pointed out that there is a general problem afflicting most such conceptions, namely the intuitive judgments that are supposed to motivate them are not universally shared. Instead of getting bogged down in what ad hoc-ness exactly means, I shift the focus of the analysis to one undesirable feature often present in alleged cases of ad hoc-ness. I call this feature the ‘monstrousness’ of a hypothesis. A fully articulated formal account of this feature is presented by specifying what it is about the internal constitution of a hypothesis that makes it monstrous. Using this account, a monstrousness measure is then proposed and somewhat sketchily
compared with the minimum description length approach.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Votsis, Ioannis
Keywords: ad hoc, scientific methodology, minimum description length, philosophy of artificial intelligence, computational science.
Subjects: General Issues > Data
Specific Sciences > Computation/Information
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Confirmation/Induction
General Issues > Reductionism/Holism
General Issues > Science vs. Pseudoscience
General Issues > Structure of Theories
General Issues > Values In Science
Depositing User: Dr Ioannis Votsis
Date Deposited: 22 Mar 2017 14:52
Last Modified: 22 Mar 2017 14:52
Item ID: 12927
Journal or Publication Title: Fundamental Issues of Artificial Intelligence
Publisher: Springer International Publishing Switzerland
DOI or Unique Handle: 10.1007/978-3-319-26485-1_18
Subjects: General Issues > Data
Specific Sciences > Computation/Information
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Confirmation/Induction
General Issues > Reductionism/Holism
General Issues > Science vs. Pseudoscience
General Issues > Structure of Theories
General Issues > Values In Science
Date: 2016
Page Range: pp. 299-313
URI: http://philsci-archive.pitt.edu/id/eprint/12927

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