Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects

Abstract

Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors' price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors' strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our selfadaptive strategies can be used to approximate equilibria in mixed strategies.

The Impact of Artificial Intelligence on International Trade: Evidence from B2C Giant E-Commerce (Amazon, Alibaba, Shopify, eBay)

“…It uses AI to analyze data from millions of listings and provide sellers with the sales price range. The chance of making a sale goes up when the price is right (Schlosser & Richly, 2019). eBay using AI fraudulent activities detect (Beutel, Akoglu, & Faloutsos, 2015).…”

Dynamic pricing under competition using reinforcement learning

“…We analyze two examples of such markets focusing on durable and replenishable goods: (i) duopolies, that require the agent to compete with a single competitor and (ii) oligopolies with multiple active competitors. Duopoly markets offer the advantage, that optimal solutions can still be computed via dynamic programming (DP), cf., e.g., Schlosser and Richly (2019), which provides an opportunity to compare and verify the results of reinforcement learning (RL).…”