A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing
实现了一种考虑消费者有限注意的随机考虑集模型，提供了参数识别条件和高效估计算法，并设计了品类优化与定价的近似算法。对美国航空数据的实证表明，该模型在约一半的市场中优于混合多项Logit模型，尤其适用于数据量较小且变化不大的场景。
Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.