A Geometric Method to Construct Minimal Peer Prediction Mechanisms
Authors: Rafael Frongillo, Jens Witkowski
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we use a geometric perspective to prove that minimal peer prediction mechanisms are equivalent to power diagrams, a type of weighted Voronoi diagram. Using this characterization and results from computational geometry, we show that many of the mechanisms in the literature are unique up to affine transformations, and introduce a general method to construct new truthful mechanisms. |
| Researcher Affiliation | Academia | Rafael Frongillo CU Boulder raf@colorado.edu Jens Witkowski ETH Zurich jensw@inf.ethco.edu |
| Pseudocode | No | The paper describes mathematical relationships and constructions but does not include any labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about making source code available or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical, focusing on mathematical proofs and characterizations. It does not use or reference any datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets, thus no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on computational experiments. Therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup. No hyperparameters, training settings, or system configurations are provided. |