Summary of The Surprising Benefits Of Base Rate Neglect in Robust Aggregation, by Yuqing Kong et al.
The Surprising Benefits of Base Rate Neglect in Robust Aggregation
by Yuqing Kong, Shu Wang, Ying Wang
First submitted to arxiv on: 19 Jun 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 The paper presents a novel approach to aggregating predictions from multiple experts without knowledge of their individual information structures. The existing methods assume that experts are Bayesian and provide predictions based on their signals, but this assumption may not hold in real-world scenarios where experts deviate systematically from Bayesian reasoning. In particular, the authors consider experts who tend to ignore the base rate. By incorporating this realistic behavior, they show that a certain degree of base rate neglect can actually improve the robustness of forecast aggregation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to combine predictions from multiple experts without knowing what kind of information each expert has. Right now, most methods assume that experts are perfect and know exactly what they’re talking about based on their own signals. But in real life, experts can be wrong or biased. The authors looked at this problem and found that if experts tend to ignore the basic facts, it can actually make combining their predictions better. |
