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.
Strategic Data Sharing between Competitors
Authors: Nikita Tsoy, Nikola Konstantinov
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 29,290 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,225 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For multiple firms, we conduct simulation studies that reveal similar trends.We use the procedure described above to empirically test the conclusions of the previous sections. |
| Researcher Affiliation | Academia | Nikita Tsoy INSAIT, Sofia University Sofia, Bulgaria EMAIL Nikola Konstantinov INSAIT, Sofia University Sofia, Bulgaria EMAIL |
| Pseudocode | No | The paper describes procedures verbally (e.g., 'standard backward induction procedure') but does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic dataset sizes from a normal distribution ('We sample m dataset sizes, one for each firm, from a distribution P = N(µ, σ2) clipped at 1 form below.') rather than using or providing access to a pre-existing public dataset. |
| Dataset Splits | No | The paper mentions 'train' and 'test' in the context of general machine learning concepts (e.g., 'train a machine learning model') but does not specify any training/validation/test dataset splits used in their own simulations or analysis. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or cloud instances) used for running its simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in their simulations. |
| Experiment Setup | Yes | We repeat the experiment 10000 times, for fixed values of m, γ, β, µ, σ and compute the mean of the average coalition size over these runs. Our simulation solves each instance of the data-sharing game exactly and average it over a big number of independent runs, which makes our results very precise. When varying one of these parameters, the default values for the other one is γ = 0.8 and β = 0.9. |