Summary of Hedging and Approximate Truthfulness in Traditional Forecasting Competitions, by Mary Monroe et al.
Hedging and Approximate Truthfulness in Traditional Forecasting Competitions
by Mary Monroe, Anish Thilagar, Melody Hsu, Rafael Frongillo
First submitted to arxiv on: 28 Sep 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 In this paper, researchers investigate the effectiveness of traditional forecasting mechanisms used in competitions. The mechanism awards points based on predictions, with the contestant earning the most points declared the winner. While it’s widely believed that contestants tend towards truthfulness as the number of events increases, a formal analysis has been lacking until now. The authors challenge this folklore by showing that even for large numbers of events, the best forecaster can still manipulate their predictions to increase their chances of winning. However, they also find that two opponents will be truthful in cases where there is sufficient uncertainty about their relative abilities and event outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a forecasting competition, people make guesses about what will happen. The person who makes the most accurate guess wins. Some people think that as more guessing events come along, people will start telling the truth because they don’t want to lose. But this paper shows that’s not true. Even if there are many guessing events, someone can still try to trick the system by making less accurate guesses. On the other hand, if two people are pretty evenly matched and aren’t sure how well each of them will do, they’ll be more likely to tell the truth. |
