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.
Implementing the Wisdom of Waze
Authors: Shoshana Vasserman, Michal Feldman, Avinatan Hassidim
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,355 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,221 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 | The paper focuses on mathematical modeling, definitions (e.g., Bayes Nash Equilibrium, Mediation Ratio), and proofs (e.g., Revelation Principle, lemmas, theorems) related to game theory and network routing. There are no sections describing empirical studies, datasets, experimental results from data analysis, performance metrics, or comparisons on real-world or simulated data in the way typical for experimental research. |
| Researcher Affiliation | Academia | Shoshana Vasserman Harvard University EMAIL Michal Feldman Tel Aviv University EMAIL Avinatan Hassidim Bar Ilan University EMAIL |
| Pseudocode | No | The paper contains mathematical formulations and proofs, but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the described methodology. |
| Open Datasets | No | As a theoretical paper, it does not involve training data or publicly available datasets for empirical experiments. |
| Dataset Splits | No | As a theoretical paper, it does not describe data splits for validation or other experimental phases. |
| Hardware Specification | No | As a theoretical paper, there is no mention of specific hardware used for running experiments. |
| Software Dependencies | No | As a theoretical paper, there is no mention of specific software dependencies or their versions. |
| Experiment Setup | No | As a theoretical paper, it does not describe an experimental setup with hyperparameters or system-level settings. |