David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Synthese 108 (1):89 - 104 (1996)
Hilary Kornblith (1993) has recently offered a reliabilist defense of the use of the Law of Small Numbers in inductive inference. In this paper I argue that Kornblith's defense of this inferential rule fails for a number of reasons. First, I argue that the sort of inferences that Kornblith seeks to justify are not really inductive inferences based on small samples. Instead, they are knowledge-based deductive inferences. Second, I address Kornblith's attempt to find support in the work of Dorrit Billman and I try to show that close attention to the workings of her computational model reveals that it does not support Kornblith's argument. While the knowledge required to ground the inferences in question is perhaps inductively derived, Billman's work does not support the notion that small samples provide a reliable basis for our generalizing inferences.
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References found in this work BETA
Alvin I. Goldman (1986). Epistemology and Cognition. Harvard University Press.
Richard E. Nisbett & Lee Ross (1980). Human Inference: Strategies and Shortcomings of Social Judgment. Prentice-Hall.
Dorrit Billman & Evan Heit (1988). Observational Learning From Internal Feedback: A Simulation of an Adaptive Learning Method. Cognitive Science 12 (4):587-625.
Paul Thagard & Richard E. Nisbett (1982). Variability and Confirmation. Philosophical Studies 42 (3):379-394.
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