Using large-scale experiments and machine learning to discover theories of human decision-making.

Journal: Science (New York, N.Y.)
PMID: 34112693

Abstract

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

Authors

  • Joshua C Peterson
    Department of Psychology, University of California, Berkeley.
  • David D Bourgin
    Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
  • Mayank Agrawal
    Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
  • Daniel Reichman
    Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Thomas L Griffiths
    Department of Psychology, University of California, Berkeley, USA.

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