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

Joshua C. Peterson, David D. Bourgin, Mayank Agrawal, Daniel Reichman, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1209-1214
Number of pages6
JournalScience
Volume372
Issue number6547
DOIs
StatePublished - Jun 11 2021

All Science Journal Classification (ASJC) codes

  • General

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