Summary of Dynamic Pricing and Learning with Long-term Reference Effects, by Shipra Agrawal et al.
Dynamic Pricing and Learning with Long-term Reference Effects
by Shipra Agrawal, Wei Tang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 A machine learning-based approach is proposed for dynamic pricing, taking into account the impact of customer price expectations on their response to current prices. The novelty lies in a simple yet effective “reference price” mechanism, where the reference price is calculated as the average of past prices offered by the seller. This differs from traditional exponential smoothing methods, which consider only recent price trends. By incorporating long-term effects of seller-offered prices on customer expectations, this mechanism aims to improve pricing strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to set prices based on what customers think they should pay is being studied. Right now, when a company sets its prices, it often looks at how much people are willing to pay for something similar in the past. This helps them figure out a good price that will make people happy and want to buy more. But sometimes this method doesn’t work as well as it could because it only looks at what happened recently. The researchers are trying to improve this by looking at prices from even farther back, so they can see if there were any patterns or trends that might help them set a better price. |
Keywords
* Artificial intelligence * Machine learning
