Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion

Researchers have investigated the role of competitor prices in demand models and the impact of data-driven pricing systems on market fairness.

Two studies examined the effects of incorporating competitor prices into demand models and the fairness implications of data-driven pricing systems. The first study found that ignoring competitor prices can lead to model misspecification and inefficiency, while strategic obliviousness may facilitate collusive outcomes and improve profits^[1]. In a competitive market, an oblivious seller must explore more aggressively to compensate for the loss of dynamic competitor information. The study also showed that in markets with both oblivious and informed sellers, the informed strictly out-earn the oblivious. A second study focused on fairness concerns in data-driven pricing systems, finding that such systems can generate discriminatory outcomes and that fairness notions can be considered in training loss, price, and demand^[2]. The researchers compared two strategies to enforce fairness in either the demand estimation stage or the price optimization stage.