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


A team of researchers from Princeton University along with one from Worcester Polytechnic Institute has developed a way to use large-scale dataset experiments and machine learning to discover new theories of decision-making. In their paper published in the journal Science, the group describes their approach to using a common tradeoff experiment to generate large datasets for use in testing and developing new theories surrounding human decision-making. Sudeep Bhatia and Lisheng He with the University of Pennsylvania and Shanghai International Studies University, respectively, have published a Perspectives piece in the same journal issue outlining current issues with decision theory and the work done by the team in this new effort.





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