Name
Abstract | ||
|---|---|---|
We study the problem of learning emph{across} a sequence of price experiments for related products, focusing on implementing the Thompson sampling algorithm for dynamic pricing. We consider a practical formulation of this problem where the unknown parameters of the demand function for each product come from a prior that is shared across products, but is unknown a priori. Our main contribution is a meta dynamic pricing algorithm that learns this prior online while solving a sequence of non-overlapping pricing experiments (each with horizon $T$) for $N$ different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (emph{meta-exploration}) with the need to leverage the current estimate of the prior to achieve good performance (emph{meta-exploitation}), and (ii) accounting for uncertainty in the estimated prior by appropriately widening the prior as a function of its estimation error, thereby ensuring convergence of each price experiment. We prove that the price of an unknown prior for Thompson sampling is negligible in experiment-rich environments (large $N$). In particular, our algorithmu0027s meta regret can be upper bounded by $widetilde{O}left(sqrt{NT}right)$ when the covariance of the prior is known, and $widetilde{O}left(N^{frac{3}{4}}sqrt{T}right)$ otherwise. Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared to prior-independent algorithms or a naive approach of greedily using the updated prior across products. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1902.10918 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamsa Bastani | 1 | 16 | 2.72 |
| David Simchi-Levi | 2 | 1449 | 151.53 |
| ruihao zhu | 3 | 14 | 3.27 |