Notice: 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. Full accuracy metrics and methodology are described in [1].
Temporal Vaccination Games under Resource Constraints
Authors: Abhijin Adiga, Anil Vullikanti
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop algorithms for ๏ฌnding NE and approximating the social optimum. We evaluate our results using simulations on different kinds of networks. |
| Researcher Affiliation | Academia | Abhijin Adiga and Anil Vullikanti , Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech Department of Computer Science, Virginia Tech EMAIL |
| Pseudocode | No | The paper describes algorithms (e.g., best-response strategy, approximation algorithm) but does not provide pseudocode blocks or clearly labeled algorithm listings. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | No | The paper uses 'three synthetic graphs: Erd os-R enyi with 100 nodes and average degree 7 (ER); random power-law graph generated using the Chung-Lu model with power-law index ฮณ = 2.5, 93 nodes and average degree 4 (CL), and a random regular graph with 100 nodes and average degree 4 (RR)'. These are graph generation models, not specific publicly accessible datasets with concrete access information (link, DOI, formal citation). |
| Dataset Splits | No | The paper describes simulation experiments and analysis of results, but it does not specify training, validation, or test dataset splits in the conventional sense of data partitioning for model training and evaluation. |
| Hardware Specification | No | The paper mentions running 'simulations on different kinds of networks' and discusses 'convergence time' but does not specify any hardware details (e.g., CPU, GPU models, memory) used for these simulations. |
| Software Dependencies | No | The paper does not mention any specific software packages, libraries, or their version numbers used in the implementation or for running experiments. |
| Experiment Setup | Yes | To keep the framework simple, we assumed uniform vaccination cost C < 1 and infection cost L = 1 for all nodes. We ran the best response algorithm for various values of B0, BT , T, and C. The results presented are averaged across 20 iterations, each producing a NE. |