Computing Nash Equilibria in Generalized Interdependent Security Games
Authors: Hau Chan, Luis E. Ortiz
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally examine and discuss the practical impact that the additional protection from transfer risk allowed in generalized IDS games has on MSNE by solving several randomly-generated instances of SC+SS-type games with graph structures taken from several real-world datasets. |
| Researcher Affiliation | Academia | Hau Chan Luis E. Ortiz Department of Computer Science, Stony Brook University {hauchan,leortiz}@cs.stonybrook.edu |
| Pseudocode | Yes | Appendixes A.1 and B of the supplementary material contain our versions of the lemmas and detailed pseudocode for the algorithm, respectively. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The underlying structures of the instances use network graphs from publicly-available, real-world datasets [6, 16 20]. |
| Dataset Splits | No | The paper describes how the experimental instances are generated and how a heuristic is run on them, but it does not specify any training, validation, or test dataset splits or cross-validation setups. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of a 'simple gradient-dynamics heuristic based on regret minimization' but does not specify any software names with version numbers for replication. |
| Experiment Setup | Yes | On each instance, we initialize the players mixed strategies uniformly at random and run a simple gradient-dynamics heuristic based on regret minimization [21 23] until we reach an (ϵ) NE. In short, we update the strategies of all non-ϵ-best-responding players i at each round t according to x(t+1) i x(t) i 10 (Mi(1, x(t) Pa(i)) Mi(0, x(t) Pa(i))). Note that for ϵ-NE to be well-defined, all Mis values are normalized. To generate each instance we generate (1) Ci/Li where Ci = 103 (1+random(0, 1)) and Li = 104 (or Li = 104/3) to obtain a low (high) cost-to-loss ratio and αi values as specified in the experiments; (2) pi such that sc i or ss i is [0, 1]; and (3) qji s consistent with probabilistic constraints relative to the other parameters (i.e. pi +P j P a(i) qji 1). |