Colin Small, a graduate student with Operations Research and Industrial Engineering, has been selected as a finalist for the 2022 Clemen–Kleinmuntz Decision Analysis Best Paper Award along with professor Eric Bickel.
Small and Bickel receive this honor for their paper "Model Complexity and Accuracy: A COVID-19 Case Study" You can read the full abstract below.
Abstract: When creating mathematical models for forecasting and decision making, there is a tendency to include more complexity than necessary, in the belief that higherfidelity models are more accurate than simpler ones. In this paper, we analyze the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts. This result may be surprising given the complexity of many COVID-19 models and their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings.
