# Forecasting Competitions with Correlated Events

arXiv: https://arxiv.org/abs/2303.13793 · HTML: https://mlsanity.com/paper/2303.13793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Incentive-Compatible Forecasting Competitions](https://mlsanity.com/paper/2101.01816/) (2021)
- [Efficient Competitions and Online Learning with Strategic Forecasters](https://mlsanity.com/paper/2102.08358/) (2021)
- [Comparing Sequential Forecasters](https://mlsanity.com/paper/2110.00115/) (2021)
- [On Learnability under General Stochastic Processes](https://mlsanity.com/paper/2005.07605/) (2020)

## Abstract (arXiv)

Beginning with Witkowski et al. [2022], recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. [2021] propose a competition mechanism based on follow-the-regularized-leader (FTRL), an online learning framework. They show that their mechanism selects an ε-optimal forecaster with high probability using only O(log(n)/ε2) events. These works, together with all prior work on this problem thus far, assume that events are independent. We initiate the study of forecasting competitions for correlated events. To quantify correlation, we introduce a notion of block correlation, which allows each event to be strongly correlated with up to b others. We show that under distributions with this correlation, the FTRL mechanism retains its ε-optimal guarantee using O(b2 log(n)/ε2) events. Our proof involves a novel concentration bound for correlated random variables which may be of broader interest.