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

Author:

Peterson Joshua C.1ORCID,Bourgin David D.1ORCID,Agrawal Mayank23ORCID,Reichman Daniel4,Griffiths Thomas L.12ORCID

Affiliation:

1. Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.

2. Department of Psychology, Princeton University, Princeton, NJ 08540, USA.

3. Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.

4. Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA.

Abstract

Discovering better theories Theories of human decision-making have proliferated in recent years. However, these theories are often difficult to distinguish from each other and offer limited improvement in accounting for patterns in decision-making over earlier theories. Peterson et al. leverage machine learning to evaluate classical decision theories, increase their predictive power, and generate new theories of decision-making (see the Perspective by Bhatia and He). This method has implications for theory generation in other domains. Science , abe2629, this issue p. 1209 ; see also abi7668, p. 1150

Funder

National Science Foundation

Defense Sciences Office, DARPA

Future of Life Institute

Nomis Foundation

Open Philanthropy Foundation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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