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2024
A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Retail Fashion
In some markets, the visual appearance of a product matters a lot. This paper investigates consumer transactions from a major fashion retailer, focusing on consumer aesthetics. Pretrained multimodal models convert images and text descriptions into high-dimensional embeddings. The value of these embeddings is verified both empirically and by their ability to segment the product space. A discrete choice model is used to decompose the distinct drivers of consumer choice: price, visual aesthetics, descriptive details, and seasonal variations. Consumers are allowed to differ in their preferences over these factors, both through observed variation in demographics and allowing for unobserved types. Estimation and inference employ automatic differentiation and GPUs, making it scalable and portable. The model reveals significant differences in price sensitivity and aesthetic preferences across consumers. The model is validated by its ability to predict the relative success of new designs and purchase patterns.
2023
Designing Auctions when Algorithms Learn to Bid: The Critical Role of Payment Rules
This paper examines the impact of different payment rules on efficiency when algorithms learn to bid. We use a fully randomized experiment of 427 trials, where Q-learning bidders participate in up to 250,000 auctions for a commonly valued item. The findings reveal that the first price auction, where winners pay the winning bid, is susceptible to coordinated bid suppression, with winning bids averaging roughly 20% below the true values. In contrast, the second price auction, where winners pay the second highest bid, aligns winning bids with actual values, reduces the volatility during learning and speeds up convergence. Regression analysis, incorporating design elements such as payment rules, number of participants, algorithmic factors including the discount and learning rate, asynchronous/synchronous updating, feedback, and exploration strategies, discovers the critical role of payment rules on efficiency. Furthermore, machine learning estimators find that payment rules matter even more with few bidders, high discount factors, asynchronous learning, and coarse bid spaces. This paper underscores the importance of auction design in algorithmic bidding. It suggests that computerized auctions like Google AdSense, which rely on the first price auction, can mitigate the risk of algorithmic collusion by adopting the second price auction.