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.
Springer, 2003
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Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.
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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.
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Optimization Workshop Notes for Mathematical Programming with Equilibrium Constraints (MPECs): Verification of MPEC Hypotheses
Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.