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 [1].
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. |