Summary of Optigrad: a Fair and More Efficient Price Elasticity Optimization Via a Gradient Based Learning, by Vincent Grari et al.
OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning
by Vincent Grari, Marcin Detyniecki
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP)
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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 This paper presents a novel approach for optimizing profit margins in non-life insurance markets, addressing three key objectives: maximizing profit margins, ensuring conversion rates, and enforcing fairness criteria like demographic parity. Traditional pricing optimization methods, which rely on linear and semi-definite programming, struggle to balance profitability and fairness. The authors propose a gradient descent-based method that directly optimizes rates in the continuous space, incorporating fairness through an adversarial predictor model. This approach reduces sequential errors, simplifies traditional models, and enforces fairness measures in commercial premium calculations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to set insurance prices so that companies make a good profit while also being fair to customers. Right now, insurance companies use old methods that don’t always work well. They have to adjust prices often and make sure they’re not favoring certain groups of people over others. The authors came up with a new approach that directly calculates the best price based on fairness rules and customer behavior. This helps ensure that insurance companies are making good decisions while also being fair. |
Keywords
» Artificial intelligence » Gradient descent » Optimization
