Strategic Data Sharing between Competitors
Authors: Nikita Tsoy, Nikola Konstantinov
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 nikita.tsoy@insait.ai Nikola Konstantinov INSAIT, Sofia University Sofia, Bulgaria nikola.konstantinov@insait.ai |
| 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. |