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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Computing Nash Equilibria in Generalized Interdependent Security Games
Authors: Hau Chan, Luis E. Ortiz
NeurIPS 2014 | Venue PDF | 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 EMAIL |
| 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). |