Stochastic Dynamic Pricing Under Duopoly Competition with Mutual Strategy Adjustments

Rainer Schlosser, Alexander Kastius

Abstract: In practical applications, firms use data-driven dynamic pricing strategies to increase their rewards in the presence of competition. Merchants are forced to steadily adjust their strategies in response to changing market environments caused by competitors that update their pricing strategies over time. In this paper, we study mutual updates of dynamic pricing strategies in an infinite horizon duopoly model with stochastic demand. We use dynamic programming techniques to compute strategies that take anticipated price reactions of the competitor into account. We consider cases in which (i) strategies are mutually observable and (ii) have to be identified from single price observations over time. For both scenarios, we analyze and compare the long-term interaction of competing self-adaptive strategies.