Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization
Authors: Adam Richardson, Boi Faltings
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct simulations using the PTNE mechanism to demonstrate the accuracy and stability of the incentives in settings with finite data for constructing models and finite peer reports. |
| Researcher Affiliation | Academia | Adam Richardson, Boi Faltings Ecole Polytechnique F ed erale de Lausanne Artificial Intelligence Laboratory |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | No | We use artificially generated data to form the true and public distributions, which can then be used to analyze expected payments and actual payments from samples. We present two data models: 1) an Empirical distribution constructed by taking finite samples with randomized frequencies, and 2) a continuous distribution constructed as a weighted sum of Gaussian distributions, or a Gaussian Mixture Model (GMM). The paper does not provide access information for these artificially generated datasets. |
| Dataset Splits | No | The paper describes simulations using artificially generated data but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | We provide details of the simulation parameters in the long version (Richardson and Faltings 2023). This indicates that specific setup details are not in the provided paper. |