Mitigating Choice Model Ambiguity: A General Framework and its Application to Assortment Optimization

In several application domains, discrete choice models have become a popular tool to accurately predict complex choice behavior within the classical predict-then-optimize paradigm. Due to a variety of possible error sources, however, estimated choice models may be subject to ambiguity, which may induce different optimal decisions of highly varying quality. While previous studies focused on … Read more