Dynamic Pricing and Learning with Discounting

Learning algorithms can take a substantial amount of time to converge, thereby raising the need to understand the role of discounting in learning. In “Dynamic Pricing and Learning with Discounting,” Z. Feng, M. Dawande, G. Janakiraman, and A. Qi examine the impact of discounting on learning by examining two classic dynamic-pricing and learning problems studied in Broder and Rusmevichientong (2012) and Keskin and Zeevi (2014) . In both settings, the retailer initially does not know the parameters of the demand model. Given a discount factor, the retailer’s objective is to determine a pricing policy to maximize the discounted revenue over a selling horizon. The authors establish lower bounds on the regret under any policy and propose new asymptotically optimal policies that take the discount factor into consideration. They numerically examine the regret under the proposed policies and the existing policies in the aforementioned two papers.