Euclidean RRM algorithms converge almost surely to the unique efficient Bayes-Nash equilibrium in a finite-dimensional approximation of Bayesian Bertrand competition with private costs.
A unified stochastic approximation framework for learning in games.Mathematical Programming, 203(1):559–609, 2024
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Convergence of Stochastic First-Order Algorithms in Bertrand Competition Under Incomplete Information
Euclidean RRM algorithms converge almost surely to the unique efficient Bayes-Nash equilibrium in a finite-dimensional approximation of Bayesian Bertrand competition with private costs.