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.
Principal-Agent Boolean Games
Authors: David Hyland, Julian Gutierrez, Michael Wooldridge
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,203 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,198 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 | In this paper, we formally define this problem and completely characterise the computational complexity of the most relevant decision problems associated with it. |
| Researcher Affiliation | Academia | 1University of Oxford, UK 2Monash University, Australia EMAIL, EMAIL |
| Pseudocode | No | The paper contains formal definitions, propositions, and theorems, but no explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor structured code-like procedures. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | As a theoretical paper, it does not involve training models on datasets, so there is no mention of publicly available or open datasets. |
| Dataset Splits | No | This theoretical paper does not conduct empirical experiments, and therefore, no training/test/validation dataset splits are mentioned. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications such as GPU or CPU models used for computational tasks. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameter values or training configurations. |