Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World
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Published:2022-06-23
Issue:3
Volume:5
Page:343-377
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ISSN:2522-0861
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Container-title:Computational Brain & Behavior
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language:en
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Short-container-title:Comput Brain Behav
Author:
Mehta Aashay, Jain Yash Raj, Kemtur Anirudha, Stojcheski Jugoslav, Consul Saksham, Tošić Mateo, Lieder FalkORCID
Abstract
AbstractTeaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decision problem that the machine learning methods were applied to. This is problematic because even domain experts cannot give complete and fully accurate descriptions of the decisions they face. To address this problem, we develop strategy discovery methods that are robust to potential inaccuracies in the description of the scenarios in which people will use the discovered decision strategies. The basic idea is to derive the strategy that will perform best in expectation across all possible real-world problems that could have given rise to the likely erroneous description that a domain expert provided. To achieve this, our method uses a probabilistic model of how the description of a decision problem might be corrupted by biases in human judgment and memory. Our method uses this model to perform Bayesian inference on which real-world scenarios might have given rise to the provided descriptions. We applied our Bayesian approach to robust strategy discovery in two domains: planning and risky choice. In both applications, we find that our approach is more robust to errors in the description of the decision problem and that teaching the strategies it discovers significantly improves human decision-making in scenarios where approaches ignoring the risk that the description might be incorrect are ineffective or even harmful. The methods developed in this article are an important step towards leveraging machine learning to improve human decision-making in the real world because they tackle the problem that the real world is fundamentally uncertain.
Funder
Bundesministerium für Bildung und Forschung Cyber Valley Research Fund Max Planck Institute for Intelligent Systems
Publisher
Springer Science and Business Media LLC
Subject
Developmental and Educational Psychology,Neuropsychology and Physiological Psychology
Reference89 articles.
1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available tensorflow.org 2. Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine 34, 6 (2017), 26–38. 3. Becker, F., Skirzyński, J., van Opheusden, B., & Lieder, F. (2022). Boosting human decision-making with AI-generated decision aids. arXiv preprint arXiv:2203.02776 4. Benartzi, S., & Thaler, R. H. Myopic loss aversion and the equity premium puzzle. The quarterly journal of Economics 110, 1 (1995), 73–92. 5. Binz, M., Gershman, S. J., Schulz, E., & Endres, D. (2022). Heuristics from bounded meta-learned inference. Psychological Review.
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