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..
Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization
Authors: Adam Richardson, Boi Faltings
AAAI 2024 | Venue PDF | 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. |