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.

An Autonomy Interrogative

“…Both fields face the same basic issues about the nature of agency and, in particular, suffer from the inadequacy of current approaches with respect to decision-making under conditions of fundamental uncertainty. Previous work [44] in the economic literature has sought to exert influence primarily on economics audiences about poor representations of human agency, has noted the role in artificial intelligence of wishful mnemonics in masking the severe limitations incumbent in standard assumptions, and has concluded that artificial intelligence has not even begun to replicate the abilities of real humans to cope with fundamental uncertainty as a result.…”

“…Smith recently argued [44] that in both economics and in artificial intelligence, the underlying assumptions driving research about agent learning and decision-making have typically neither sufficiently nor even explicitly emphasised the significance of fundamental uncertainty; current models remain structurally very similar to those of the past. We remain largely tied to probabilistic and statistical methods of learning and reasoning and thus to their inherent limits.…”

“…In particular, I am concerned with autonomy as particularly featuring decisions about the allocation of self under uncertainty, and hypothesise that self-allocation corresponds with the same boundary that delineates a notion of uncertainty that has come to be known as ontological uncertainty [13,25,30,44,50]. Interacting selfallocating agents entails logical paradox in general, which underpins formal limits on knowledge and thus the intrinsic uncertainty of such systems.…”

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…Most articles in this category claim that the ''unique strengths of humans and AI can act synergistically'' (Jarrahi 2018: 579), implying that through the combination of human and AI capabilities, efficiency and profitability in decision making are expected to increase (Smith 2016;Anderson 2019;Shrestha et al 2019). Furthermore, it is widely agreed that humans and machines can augment each other, implying that AI systems learn from human inputs and vice versa (Jarrahi 2018;Schneider and Leyer 2019).…”

“…Thus, using AI to automate some tasks of the decision-making process gives people time to invest in those skills that AI cannot adequately perform, but which are critical to strategic decisions. The other authors further argue that humans are better at judgment, the analysis of political situations, psychological influences, flexibility, creativity, visionary thinking, and equivocality (Parry et al 2016;Smith 2016;Rezaei et al 2017;Jarrahi 2018;Agrawal et al 2019;Shrestha et al 2019). In addition, ''even if machines can determine the optimal decision, they are less likely to be able to sell it to a diverse set of stakeholders.''…”