Model Uncertainty and the Crisis in Science

By Cristobal Young. 2018

47 citations recorded by the source corpus.

The “crisis in science” today is rooted in genuine problems of model uncertainty and lack of transparency. Researchers estimate a large number of models in the course of their research but only publish a small number of preferred results. Authors have much influence on the results of an empirical study through their choices about model specification. I advance methods to quantify the influence of the author—or at least demonstrate the scope an author has to choose a preferred result. Multimodel analysis, combined with modern computational power, allows authors to present their preferred estimate alongside a distribution of estimates from many other plausible models. I demonstrate the method using new software and applied empirical examples. When evaluating research results, accounting for model uncertainty and model robustness is at least as important as statistical significance.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/355afe32bddf7a14b44f2134b4652dff10f0a663