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arXiv Machine Learning By Elnaz Nowrouzi, Zhiqun Zuo, Xueru Zhang, Mohammad Mahdi Khalili

BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2608. 14856v1 Announce Type: cross Abstract: Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: best-response dynamics may need hundreds of iterations per instance, and downstream tasks such as auditing, stress-testing, and incentive design often require repeatedly re-solving the game under parameter perturbations.

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