Summary of Robust Decision Aggregation with Adversarial Experts, by Yongkang Guo et al.
Robust Decision Aggregation with Adversarial Experts
by Yongkang Guo, Yuqing Kong
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 robust aggregation method for combining expert opinions, where some experts are truthful while others are adversarial. It aims to find an optimal aggregator that minimizes regret, defined as the difference in expected loss between the aggregator’s forecast and a benchmark that optimally aggregates reports from truthful experts. The proposed method assumes marginally symmetric experts with shared common prior and marginal posteriors. The authors focus on designing an aggregator that can predict the true world state from expert reports without knowledge of the underlying information structures or adversarial strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to combine expert opinions when some experts are truthful and others might be trying to manipulate the outcome. It wants to find a way to combine these opinions in a way that minimizes mistakes compared to what would happen if all the experts were telling the truth. The authors assume that all experts start with the same understanding of the world, but they can have different opinions based on their private information. The goal is to design an algorithm that can predict the true state of the world from these expert opinions without knowing how each expert got their opinion. |
