7,156 research outputs found
Variational Inequality Approach to Stochastic Nash Equilibrium Problems with an Application to Cournot Oligopoly
In this note we investigate stochastic Nash equilibrium problems by means of
monotone variational inequalities in probabilistic Lebesgue spaces. We apply
our approach to a class of oligopolistic market equilibrium problems where the
data are known through their probability distributions.Comment: 19 pages, 2 table
Distributed Learning for Stochastic Generalized Nash Equilibrium Problems
This work examines a stochastic formulation of the generalized Nash
equilibrium problem (GNEP) where agents are subject to randomness in the
environment of unknown statistical distribution. We focus on fully-distributed
online learning by agents and employ penalized individual cost functions to
deal with coupled constraints. Three stochastic gradient strategies are
developed with constant step-sizes. We allow the agents to use heterogeneous
step-sizes and show that the penalty solution is able to approach the Nash
equilibrium in a stable manner within , for small step-size
value and sufficiently large penalty parameters. The operation
of the algorithm is illustrated by considering the network Cournot competition
problem
Sample-path solutions for simulation optimization problems and stochastic variational inequalities
inequality;simulation;optimization
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