Summary of On the Price Of Exact Truthfulness in Incentive-compatible Online Learning with Bandit Feedback: a Regret Lower Bound For Wsu-ux, by Ali Mortazavi et al.
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
by Ali Mortazavi, Junhao Lin, Nishant A. Mehta
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)
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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 studies a strategic variant of the classical game of prediction with expert advice, where each expert is selfish and reports their belief to maximize their expected future reputation. The goal is to design an algorithm that is both incentive-compatible (truthful) and achieves sublinear regret compared to the expert with the best belief. Building on prior work on wagering mechanisms, the authors obtained truthful no-regret algorithms in both full information and bandit settings. However, the known regret bound for the bandit algorithm WSU-UX is O(T^(2/3)), which does not match the minimax rate for the classical setting. The paper shows that WSU-UX suffers a worst-case lower bound of Ω(T^(2/3)) through explicit construction of loss sequences. While left open is the possibility of an IC algorithm achieving O(sqrt(T)) regret, WSU-UX remains a natural choice due to the limited design room for IC algorithms in this setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how experts work together to make predictions and decisions. In this game, each expert tries to make themselves look good by reporting what they think is most likely to happen. The goal is to create an algorithm that makes smart choices while also being fair to all the experts involved. The researchers found a way to do this using special rules called wagering mechanisms. They tested their approach in different situations and saw that it worked well, but there’s still room for improvement. |
