Idealizations of Uncertainty, and Lessons from Artificial Intelligence

Robert Elliott Smith

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), 2016, vol. 10, No 2016-7, 40 pages

Abstract: At a time when economics is giving intense scrutiny to the likely impact of artificial intelligence (AI) on the global economy, this paper suggests the two disciplines face a common problem when it comes to uncertainty. It is argued that, despite the enormous achievements of AI systems, it would be a serious mistake to suppose that such systems, unaided by human intervention, are as yet any nearer to providing robust solutions to the problems posed by Keynesian uncertainty. Under the radically uncertain conditions, human decision-making (for all its problems) has proved relatively robust, while decision making relying solely on deterministic rules or probabilistic models is bound to be brittle. AI remains dependent on techniques that are seldom seen in human decision-making, including assumptions of fully enumerable spaces of future possibilities, which are rigorously computed over, and extensively searched. Discussion of alternative models of human decision making under uncertainty follows, suggesting a future research agenda in this area of common interest to AI and economics.

Keywords: uncertainty; probability; Bayesian; artificial intelligence (search for similar items in EconPapers)
JEL-codes: B59 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifweej:20167

DOI: 10.5018/economics-ejournal.ja.2016-7

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