The Economics of Professional Decision-Making: Can Artificial Intelligence Reduce Decision Uncertainty?
Annual Review of Economics · 2026 · W. Bentley MacLeod
中文摘要
本文提出一个经济模型，为整理关于医生和法官绩效的日益增长的文献提供了一个框架。这些专业人员的主要任务是根据其客户提供的信息做出决策。本文借助 Kahneman（2011）所称的快决策与慢决策——即认知科学中的系统 1 与系统 2——来讨论专业决策。慢决策对应于经济学家的理性选择模型，而系统 1（快）决策则是由训练和人力资本引导的高速、直觉性选择。本文利用这一区分，基于 Bewley（2011）的奈特不确定性理论，构建了一个不确定性下的决策模型，以表明人类价值观是最优选择的必要投入。这进而给出了人工智能（AI）工具能够辅助专业决策的条件，同时指出了此类工具需要明确纳入人类价值观才能做出更好决策的情形。
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.