Opening the Opaque-Box: Strength, Limitations, and Recommendations in the Application of Scientific Regret Minimization to Behavioral Modeling

Virtual · May 2021

Posters · Methodology

Alexandre Filipowicz
Toyota Research Institute
Totte Harinen
Toyota Research Institute
Rumen Iliev
Toyota Research Institute
Yanxia Zhang
Toyota Research Institute
Yan-Ying Chen
Toyota Research Institute
Yue Weng
Toyota Research Institute
Abishek Komma
Toyota Research Institute
Kent Lyons
Toyota Research Institute
Charlene Wu
Toyota Research Institute

Abstract

The ‘Scientific Regret Minimization’ method proposes steps to use machine learning in the development of precise, interpretable models of behavior. Using simulations, we highlight strengths and limitations with this method and offer solutions to leverage the predictive power of machine learning in the development and improvement of behavioral models.

Decision Making

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Opening the Opaque-Box: Strength, Limitations, and Recommendations in the Application of Scientific Regret Minimization to Behavioral Modeling

Abstract

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