Overview
As automation transforms the nature of work, many roles increasingly depend on people’s ability to plan, prioritze, and make decisions in complex and uncertain situations. The project investigated whether machine learning and computational models of human cognition could be used to improve these abilities.
Led by Falk Lieder, the project developed a scalable approach for discovering and teaching effective human decision-making strategies. Rather than using AI to replace human decision-makers, the research uses AI to identify strategies that people can learn and apply themselves. The approach is intended to support planning, prioritization, project selection, and other forms of self-management.
Key Results
- Developed AI-based methods for automatically discovering efficient decision-making and planning strategies.
- Created intelligent tutoring approaches that teach strategies through explanations, examples, and practice.
- Demonstrated improved decision-making in controlled evaluation tasks compared to baseline approaches.
- Applied methods to scenarios ranging from abstract planning tasks to realistic project selection problems.
- Showed that participants using AI-supported tutorials made better decisions than those training independently.
Why it matters
The research suggests that AI can be used not only to automate decisions but also to improve people’s own decision-making abilities. It therefore has potential applications in education, professional training, and organizational decision-making.
Publications & Outputs
- Consul, S., Heindrich, L., Stojcheski, J., & Lieder, F. (2022), Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning, Computational Brain & Behavior, 5(2), 185-216. https://doi.org/10.1007/s42113-022-00128-3
- Callaway, F., Jain, Y. R., van Opheusden, B., Das, P., Iwama, G., Gul, S., Krueger, P.M., Becker, F., Griffiths, T. L., & Lieder, F. (2022). Leveraging Artificial Intelligence to Improve People’s Planning Strategies. PNAS, 119 (12) e2117432119. https://doi.org/10.1073/pnas.2117432119
- Mehta, A., Jain, Y. R., Kemtur, A., Stojcheski, J., Consul, S., Tošić, M., & Lieder, F. (2022). Leveraging machine learning to automatically derive robust decision strategies from imperfect knowledge of the real world. Computational Brain & Behavior, 5(3), 343-377. https://doi.org/10.1007/s42113-022-00141-6
- Becker, F., Skirzyński, J., van Opheusden, B., & Lieder, F. (2022). Boosting human farsightedness with automatically discovered planning instructions. Computational Brain & Behavior, 5(4), 467–490. https://doi.org/10.1007/s42113-022-00149-y
- Skirzynski, J., Jain, Y. R., & Lieder, F. (2024). Automatic discovery and description of human planning strategies. Behavior Research Methods, 56(3), 1065-1103. https://doi.org/10.3758/s13428-023-02062-z
