Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Forecasting Competitions with Correlated Events
Authors: Rafael Frongillo, Manuel Lladser, Anish Thilagar, Bo Waggoner
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the ο¬rst accuracy and approximate truthfulness guarantees for forecasting competitions with 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 and weakly correlated with the rest. We show that under distributions with this correlation, the Multiplicative Weights mechanism retains its Ο΅-optimal guarantee using O(b2 log(n)/Ο΅2) events. Our proof involves a novel concentration bound for correlated random variables which may be of broader interest. |
| Researcher Affiliation | Academia | Rafael Frongillo, Manuel Lladser, Anish Thilagar, Bo Waggoner University of Colorado Boulder |
| Pseudocode | No | The paper describes mathematical mechanisms and proofs but does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It discusses 'events' and 'distributions' as part of its theoretical framework. |
| Dataset Splits | No | The paper does not involve empirical experiments using datasets, and therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies or specific version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and mechanism design, without describing any empirical experimental setup or hyperparameter details. |