Equilibria in Epidemic Containment Games
Authors: Sudip Saha, Abhijin Adiga, Anil Vullikanti
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the characteristics of Nash equilibria empirically in different real communication and infrastructure networks, and find that our analytical results can help explain some of the empirical observations. |
| Researcher Affiliation | Academia | Sudip Saha, Abhijin Adiga and Anil Kumar S. Vullikanti Department of Computer Science Network Dynamics and Simulation Science Laboratory Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060 Email: {ssaha,abhijin,akumar}@vbi.vt.edu |
| Pseudocode | No | The paper describes strategies and procedures in prose (e.g., 'iterative strategies for finding NE by removing nodes in decreasing or increasing order of their degrees') but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions supplementary material is 'available at http://staff.vbi.vt.edu/ssaha/papers/ecgame extended.pdf', which is a PDF document, not a code repository. No other statements are made about releasing source code for their methodology. |
| Open Datasets | Yes | Table 1: Networks used in our experiments and their relevant properties: Five real (Leskovec 2011; Opsahl and Panzarasa 2009) and two synthetic graphs. |
| Dataset Splits | No | The paper does not provide specific details about train/validation/test dataset splits. It discusses empirical analysis on entire networks rather than using partitioned datasets for model training and evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., specific GPU/CPU models, memory details) used to run its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software details, such as library or solver names with version numbers, used to replicate the experiments. |
| Experiment Setup | Yes | An instance of the EC game is defined by the tuple (G, T, C, L, Le). For a strategy profile a, the social cost cost(a) = P v V cost(v, a). If the epidemic dies out quickly, then cost(a) = |S|C + |V S|L where S = S(a); otherwise, cost(a) = |S|C + |Ve|Le + |V S Ve|L, where Ve is the set of insecure nodes x that are part of those components of the attack graph where the epidemic lasts long (i.e. λ1(Gx[V S(a)]) T). ... We estimate the maximum and minimum NE cost by two heuristics, that we call the High Degree (HDG) and Low Degree (LDG) strategies; these are obtained by running iterative strategies for finding NE by removing nodes in decreasing or increasing order of their degrees... (Figure 4 caption) threshold set to T = 0.3λ1(G). |