Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Facility Location Games with Entrance Fees
Authors: Mengfan Ma, Mingyu Xiao, Tian Bai, Bakh Khoussainov
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,595 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,582 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We systematically study the model and design strategyproof mechanisms with nice approximation ratios and also complement these with nearly-tight impossibility results. Specifically, for one-facility and two-facility games, we provide upper and lower bounds for the approximation ratios given by deterministic and randomized mechanisms, with respect to the utilitarian and egalitarian objectives. Most of our bounds are tight, and these bounds are independent of the entrance fee functions. |
| Researcher Affiliation | Academia | Mengfan Ma, Mingyu Xiao , Tian Bai, Bakh Khoussainov School of Computer Science and Engineering, University of Electronic Science and Technology of China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper contains mathematical formulations, theorems, and proofs but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve training models on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve validation datasets or splits. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software dependencies with version numbers for implementation or experiments. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |