Introduction: 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

The judges for the 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application is a system for optimally managing Dow Agrosciences’ (now Corteva) seed corn portfolio, which includes seeds for several hundred varieties of corn and is valued at more than $1 billion. The model employs Bayesian analytic methods to estimate crop yields from expert judgement. Stochastic optimization is then used to determine backup production in South America while dealing with yield uncertainty in North America. The remaining four papers include an efficient mixed-integer program used by Birchbox to determine individualized subscriber product sets; a scheduling system for Argentina’s premier soccer league; an incentive system for encouraging Lyft drivers to reposition to provide improved service; and a system for optimizing the electric bus network design in Rotterdam.

High BMI Predicts Attention to Less Healthy Product Sets: Can a Prompt Lead to Consideration of Healthier Sets of Products?

While the food environment has been implicated in diet-related health disparities, individuals’ ability to shape the food environment by limiting attention to a subset of products has not been studied. We examine the relationship between BMI category and consideration set—the products the individual considers before making a final choice—in an online hypothetical shopping experiment. Specifically, we focus on the healthiness of the consideration set the individual selected. Secondly, we examined the interaction of a health prompt (versus a no-prompt control) with BMI category on the healthiness of the consideration set. We used linear probability models to document the relationship between weight status and consideration set, between prompt and consideration set, and the effect of the interaction between prompt and weight status on consideration set. We found that (1) obese individuals are 10% less likely to shop from a consideration set that includes the healthy options, (2) viewing the prompt increased the probability of choosing a healthy consideration set by 9%, and (3) exposure to the prompt affected individuals in different BMI categories equally. While obese individuals are more likely to ignore healthier product options, a health-focused prompt increases consideration of healthy options across all BMI categories.