Summary of A Tale Of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing, by Zeyu Bian et al.
A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing
by Zeyu Bian, Zhengling Qi, Cong Shi, Lan Wang
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to offline dynamic pricing that does not rely on data coverage assumptions. It addresses the challenge of making decisions without knowing the optimal prices by framing the problem as a partial identification framework. The authors establish a partial identification bound for the demand parameter and propose pessimistic and opportunistic strategies within this framework. They theoretically prove rate-optimal finite-sample regret guarantees for both strategies and empirically demonstrate their superior performance in a synthetic environment. This research provides valuable insights into offline pricing strategies, ultimately fostering sustainable growth and profitability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in online pricing where we don’t have data on all prices. Before, people thought that the best price would be observed in the data, but this isn’t true. The authors come up with a new way to think about this problem called partial identification. They use this idea to find a range of possible demand values and then propose two strategies: one that is careful and one that takes risks. The paper shows that these strategies work well and can help companies make good decisions without having all the data. |
