Optimal Pricing with a Single Point
نویسندگان
چکیده
We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at single historical price? The optimizes over (potentially randomized) policies to maximize worst-case ratio of revenue it can garner compared an oracle with full knowledge distribution values, when latter is only assumed belong broad non-parametric set. In particular, our framework applies widely used regular and monotone non-decreasing hazard rate (mhr) classes distributions. For settings where seller knows exact probability sale associated one or confidence interval for it, we fully characterize optimal performance near-optimal algorithms that adjust information hand. As examples, against mhr distributions, show possible guarantee 85% if half customers have bought price, 1% bought, still 51% performance. develop leads new insights value pricing, as well randomization. addition, general allows deterministic mechanisms incorporate uncertainty in sale.
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ژورنال
عنوان ژورنال: Social Science Research Network
سال: 2021
ISSN: ['1556-5068']
DOI: https://doi.org/10.2139/ssrn.3801056