Utility Fairness in Contextual Dynamic Pricing with Demand Learning
Xi Chen David Simchi-Levi Yining Wang
This paper introduces a novel contextual bandit algorithm for personalized
pricing under utility fairness constraints in scenarios with uncertain demand,
achieving an optimal regret upper bound. Our approach, which incorporates
dynamic pricing and demand learning, addresses the critical challenge of
fairness in pricing strategies. We first delve into the static full-information
setting to formulate an optimal pricing policy as a constrained optimization
problem. Here, we propose an approximation algorithm for efficiently and
approximately computing the ideal policy.
  We also use mathematical analysis and computational studies to characterize
the structures of optimal contextual pricing policies subject to fairness
constraints, deriving simplified policies which lays the foundations of more
in-depth research and extensions.
  Further, we extend our study to dynamic pricing problems with demand
learning, establishing a non-standard regret lower bound that highlights the
complexity added by fairness constraints. Our research offers a comprehensive
analysis of the cost of fairness and its impact on the balance between utility
and revenue maximization. This work represents a step towards integrating
ethical considerations into algorithmic efficiency in data-driven dynamic
pricing.