arbiter
JournalsAccount

The Economics of Professional Decision-Making: Can Artificial Intelligence Reduce Decision Uncertainty?

W. Bentley MacLeod

Annual Review of Economics2026https://doi.org/10.1146/annurev-economics-051624-071111article
AJG 3ABDC A*
Weight
0.50

Abstract

This article outlines an economic model that provides a framework for organizing the growing literature on the performance of physicians and judges. The primary task of these professionals is to make decisions based on the information provided by their clients. The article discusses professional decisions in terms of what Kahneman (2011) calls fast and slow decisions, known as System 1 and System 2 in cognitive science. Slow decisions correspond to the economist's model of rational choice, while System 1 (fast) decisions are high-speed, intuitive choices guided by training and human capital. This distinction is used to provide a model of decision-making under uncertainty based on Bewley’s (2011) theory of Knightian uncertainty to show that human values are an essential input to optimal choice. This, in turn, provides conditions under which artificial intelligence (AI) tools can assist professional decision-making, while pointing to cases in which such tools need to explicitly incorporate human values in order to make better decisions.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1146/annurev-economics-051624-071111

Or copy a formatted citation

@article{w.2026,
  title        = {{The Economics of Professional Decision-Making: Can Artificial Intelligence Reduce Decision Uncertainty?}},
  author       = {W. Bentley MacLeod},
  journal      = {Annual Review of Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1146/annurev-economics-051624-071111},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

The Economics of Professional Decision-Making: Can Artificial Intelligence Reduce Decision Uncertainty?

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.