Incentive-Compatible Forecasting Competitions
Authors: Jens Witkowski, Rupert Freeman, Jennifer Vaughan, David Pennock, Andreas Krause
AAAI 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We lower-bound the probability that our mechanism selects the most accurate forecaster, and give rates for how quickly this bound approaches 1 as the number of events grows. Our techniques can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events. |
| Researcher Affiliation | Collaboration | Jens Witkowski ETH Zurich EMAIL Rupert Freeman Duke University EMAIL Jennifer Wortman Vaughan Microsoft Research EMAIL David M. Pennock Microsoft Research EMAIL Andreas Krause ETH Zurich EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions external platforms like Netflix Prize and Kaggle, but no code release for their own work. The conclusion also states that evaluating ELF experimentally is a "future research direction", implying code is not yet public. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets, thus no information on public dataset availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, thus no information on training/validation/test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |