Leveraging artificial intelligence to improve people’s planning strategies

Author:

Callaway Frederick1ORCID,Jain Yash Raj2ORCID,van Opheusden Bas1,Das Priyam3ORCID,Iwama Gabriela2,Gul Sayan4,Krueger Paul M.5ORCID,Becker Frederic2ORCID,Griffiths Thomas L.15,Lieder Falk2ORCID

Affiliation:

1. Department of Psychology, Princeton University, Princeton, NJ 08540

2. Rationality Enhancement Group, Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany

3. Department of Cognitive Sciences, University of California, Irvine, CA 92697-5100

4. Department of Psychology, University of California, Berkeley, CA 94720-1650

5. Department of Computer Science, Princeton University, Princeton, NJ 08540

Abstract

Significance Many 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.

Funder

DOD | United States Navy | Office of Naval Research

Templeton World Charity Foundation

Cyber Valley Research Fund

DOD | USAF | AFMC | Air Force Office of Scientific Research

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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4. Y. R. Jain, F. Callaway, F. Lieder, “Measuring how people learn how to plan” in CogSci 2019, A. Goel, C. Seifert, C. Freksa, Eds. (Cognitive Science Society, Austin, TX, 2019), pp. 1956–1962.

5. Individual differences in adult decision-making competence.

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