Journal Article
No. 2016-7 | March 21, 2016
Idealizations of Uncertainty, and Lessons from Artificial Intelligence

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.

JEL Classification:

B59

Assessment

  • Downloads: 1150 (Discussion Paper: 791)

Links

Cite As

Robert Elliott Smith (2016). Idealizations of Uncertainty, and Lessons from Artificial Intelligence. Economics: The Open-Access, Open-Assessment E-Journal, 10 (2016-7): 1–40. http://dx.doi.org/10.5018/economics-ejournal.ja.2016-7


Comments and Questions


David Marsay - Logic and Mathematics
March 31, 2016 - 00:16

Superficially, much of this paper appears to contradict many of my own views, as in my 2016-1. On the other hand, CNT does fit my own experience. In a similar vein, I note that if all references to logic and mathematics were caveated with ‘as understood by most mainstream economists’ ...[more]

... then this paper would be complementary rather than contradictory to my own. Indeed, its critique of the use of logic and mathematics by economists and AI practitioners can be read as a good guide to the kind of logic and mathematics (and intelligence and AI) that will be needed for a reformed economics capable of addressing issues of radical uncertainty.


Robert Smith - Logic and Mathematics
April 07, 2016 - 11:17

I certainly see Marsay 2016 (a excellent paper, in my opinion) as presenting complementary ideas to my paper here, just from a different perspective.