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DTSTART;TZID=America/New_York:20190409T160000
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DTSTAMP:20220523T204904
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SUMMARY:Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity
DESCRIPTION:We consider a seller who can dynamically adjust the price of a product at the individual customer level\, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model\, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship between the customer features and the product demand\, but learns this through sales observations over a selling horizon of $T$ periods. We prove that the seller’s expected regret\, i.e.\, the revenue loss against a clairvoyant who knows the underlying demand relationship\, is at least of order $s\sqrt{T}$ under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order $s\sqrt{T}log(T)$. We extend this policy to a more realistic setting where the seller does not know the true demand predictors\, and show this policy has an expected regret of order $s\sqrt{T}(log(d)＋log(T))$\, which is also near-optimal. Finally\, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets\, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then-optimize policies. Furthermore\, our policy significantly improves upon the loan company’s historical pricing decisions in terms of annual expected revenue. \nBio: Gah-Yi Ban is an Assistant Professor of Management Science and Operations at London Business School. Gah-Yi’s research is in Big Data analytics\, specifically decision-making with complex\, high-dimensional and/or highly uncertain data with applications to operations management and finance. Gah-Yi’s research has appeared on most-downloaded lists of Management Science and Operations Research\, and awarded Honorable Mention in 2018 INFORMS JFIG Paper Competition. Gah-Yi graduated from UC Berkeley with MSc/MA/PhD in Industrial Engineering/ Statistics/Operations Research. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing.
URL:https://idss.mit.edu/calendar/lids-seminar-gah-yi-ban-london-business-school/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
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