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Abstract
In building models that causally explain observed data and future data, econometricians must grapple with quantifiable uncertainty, or risk, and unquantifiable Knightian uncertainty, or ambiguity. In contrast, machine learning practitioners work with statistical models for a data set that enable predictions about data items imputed to be in the data set. Recently these two distinct modeling concepts have become topics of mutual interest in economics and machine learning. We take the viewpoint here that a data set implicitly embodies the ambiguity of the generating processes from which it arises. We present a data model incorporating ambiguity that we dub theInscrutable Decision Maker (IDM) derived from the Anscombe-Aumann model of subjective utility.
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School of Electrical Engineer and Computer Science, Oregon State University, Corvallis, OR, 97331, USA
Rick Hangartner & Paul Cull
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Correspondence toRick Hangartner.
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University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
Roberto Moreno-Díaz
Johannes Kepler University Linz, Linz, Austria
Franz Pichler
University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
Alexis Quesada-Arencibia
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Hangartner, R., Cull, P. (2018). Inscrutable Decision Makers: Knightian Uncertainty in Machine Learning. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_28
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