Simple is Enough: A Cascade Approximation for Attention-Based Satisficing Choice Models
Empirical evidence suggests that consumers commonly focus their attention on a subset of available products and evaluate them in batches to identify a satisfactory option. To capture this phenomenon, we introduce the attention-based satisficing choice rule, which encompasses special cases such as the sequential MNL (e.g., Gao et al. 2021), click-based MNL (e.g., Aouad et al. 2019), and random consideration set models (e.g., Gallego and Li 2017). Through an empirical investigation employing data sourced from Expedia, we provide evidence that special cases of the proposed model exhibit a notable advantage in terms of predictive accuracy when compared to the mixed MNL. Notwithstanding the NP-hardness of finding the revenue-maximizing assortment and estimating certain parameters for the proposed model, we demonstrate that it can be approximated by a simple cascade model (e.g., Kempe and Mahdian 2008) with substantially fewer parameters. Specifically, we establish that the overall likelihood of purchasing from any given assortment under the proposed model can be estimated within a certain range, multiplied by that in the cascade model;
moreover, by utilizing the readily computable optimal assortment derived from the approximated model as a heuristic, we show that the worst-case revenue given partial information is at least 3/8 of that obtained from an optimized assortment under the best parameter configuration. Finally, based on the technique established in this study, we extend the analysis to the constrained assortment optimization problem, the categorized attention-based assortment optimization problem, and the joint assortment and pricing problem.
Gao Pin is currently an assistant professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received a B.S. in physics from Wuhan University in 2013 and a M.Phil. in physics from Hong Kong University of Science and Technology (HKUST) in 2015, after which he worked in industry for two years. In 2021, he received a Ph.D. in Industry Engineering and Decision Analytics from HKUST. Dr. Gao's current research interests include revenue management and operations management.