In this article, the team presents their model discovery framework, using Genetic programming to automate the search across multiple candidate model structures from an existing human-built generative model of alcohol-use patterns in the USA.
They use a multiobjective approach, which enables multiple perspectives on the value of any particular model. In the paper, they demonstrate that the framework is successful in identifying three novel competing explanations of alcohol-use patterns not previously considered by the human modeler.