  Imagine you and a friend purchase identical items at a store, yet only your
friend received a discount. Would your friend's discount make you feel unfairly
treated by the store? And would you be less willing to purchase from that store
again in the future? Based on a large-scale online survey that we ran on
Prolific, it turns out that the answers to the above questions are positive.
Motivated by these findings, in this work we propose a notion of individual
fairness in online revenue management and an algorithmic module (called ``Grace
Period'') that can be embedded in traditional revenue management algorithms and
guarantee individual fairness. Specifically, we show how to embed the Grace
Period in five common revenue management algorithms including Deterministic
Linear Programming with Probabilistic Assignment, Resolving Deterministic
Linear Programming with Probabilistic Assignment, Static Bid Price Control,
Booking Limit, and Nesting, thus covering both stochastic and adversarial
customer arrival settings. Embedding the Grace Period does not incur additional
regret for any of these algorithms. This finding indicates that there is no
tradeoff between a seller maximizing their revenue and guaranteeing that each
customer feels fairly treated.
