The Exponomial Choice Model: A New Alternative for Assortment and Price Optimization
研究了一种基于负偏态效用分布的指数选择模型，该模型在最优定价和品种规划中允许可变加价和价格跳跃，弥补了多项Logit模型的局限，并提供了估计方法。
We investigate the use of a canonical version of a discrete choice model due to Daganzo (1979) [Daganzo C (1979) Multinomial Probit: The Theory and Its Application to Demand Forecasting (Academic Press, New York).] in optimal pricing and assortment planning. In contrast to multinomial and nested logit (the prevailing choice models used for optimizing prices and assortments), this model assumes a negatively skewed distribution of consumer utilities, an assumption we motivate by conceptual arguments as well as published work. The choice probabilities in this model can be derived in closed form as an exponomial (a linear function of exponential terms). The pricing and assortment planning insights we obtain from the exponomial choice (EC) model differ from the literature in two important ways. First, the EC model allows variable markups in optimal prices that increase with expected utilities. Second, when prices are exogenous, the optimal assortment may exhibit leapfrogging in prices, i.e., a product can be skipped in favor of a lower-priced one depending on the utility positions of neighboring products. These two plausible pricing and assortment patterns are ruled out by multinomial logit (and by nested logit within each nest). We provide structural results on optimal pricing for monopoly and oligopoly cases, and on the optimal assortments for both exogenous and endogenous prices. We also demonstrate how the EC model can be easily estimated—by establishing that the log-likelihood function is concave in model parameters and detailing an estimation example using real data.