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].
The Surprising Power of Hiding Information in Facility Location
Authors: Safwan Hossain, Evi Micha, Nisarg Shah2168-2175
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results provide a complete picture of the power of strategyproof mechanisms eliciting different levels of information and with respect to each type of manipulation. Surprisingly, we show that in some cases hiding locations can be a strictly more powerful manipulation than misreporting locations. |
| Researcher Affiliation | Academia | Safwan Hossain Vector Institute, University of Toronto EMAIL Evi Micha University of Toronto EMAIL Nisarg Shah University of Toronto EMAIL |
| Pseudocode | Yes | Algorithm 1: Mechanism PROJECT-AND-FIT |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the release of source code for the methodology described. |
| Open Datasets | No | The paper conducts theoretical analysis and does not involve training models on datasets. Therefore, no information about publicly available or open training datasets is provided. |
| Dataset Splits | No | The paper presents theoretical research, not empirical studies involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementation or dependencies with version numbers for reproducible experiments. |
| Experiment Setup | No | The paper presents theoretical analysis and does not include details on experimental setup, hyperparameters, or training configurations. |