Discovering Rational Heuristics for Risky Choice

2022

Article

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For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

@article{Krueger2022Discovering,
  title = {Discovering Rational Heuristics for Risky Choice},
  author = {Krueger, P. and Callaway, F. and Gul, S. and Griffiths, T. and Lieder, F.},
  journal = {PsyArXiv Preprints},
  month = jan,
  year = {2022},
  doi = {10.31234/osf.io/mg7dn},
  url = {https://psyarxiv.com/mg7dn},
  month_numeric = {1}
}