Callaway Frederick, Jain Yash Raj, van Opheusden Bas, Das Priyam, Iwama Gabriela, Gul Sayan, Krueger Paul M, Becker Frederic, Griffiths Thomas L, Lieder Falk
Department of Psychology, Princeton University, Princeton, NJ 08540.
Rationality Enhancement Group, Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany.
Proc Natl Acad Sci U S A. 2022 Mar 22;119(12):e2117432119. doi: 10.1073/pnas.2117432119. Epub 2022 Mar 16.
SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.
意义
如果人们采用最优决策策略,许多糟糕的决策及其灾难性后果是可以避免的。在此,我们引入一种有原则的计算方法来改善人类决策。基本思路是就人们如何做出决策给予他们反馈。我们开发了一种利用人工智能以让人们快速发现最佳决策策略的方式来生成这种反馈的方法。我们的实证研究结果表明,一种有原则的计算方法会带来决策能力的提升,这种提升能迁移到更复杂环境中更具挑战性的决策上。从长远来看,这一系列工作可能会催生一些应用程序,这些程序能教会人们决策、推理、设定目标、规划及实现目标的巧妙策略。