Pricing is central to many industries and academic disciplines ranging from Operations Research to Computer Science and Economics. In this talk, we study data-driven optimal pricing in low informational environments. We analyze how a decision-maker should price based on a single sample of the willingness-to-pay (WTP) of customers. The decision-maker's objective is to select a general pricing policy with maximum competitive ratio when the WTP distribution is only known to belong to some broad set.
