In a previous work, we have introduced an algorithm, called RaBVItG, used for computing Feedback Nash equilibria of deterministic multiplayers Differential Games. This algorithm is based on a sequence of Game Iterations (i.e., a numerical method to simulate an equilibrium of a Differential Game), combined with Value Iterations (i.e, a numerical method to solve a Dynamic Programming equation associated to a Differential Game). Here, our main objective is to present an extension of this algorithm, called RaBVIt-SG, considering stochastic multiplayers Differential Games. More precisely, we consider Differential Games including a multivariate Brownian term in the diffusive part of the Stochastic Differential Equations system. Hence, with respect to the previous deterministic version of the algorithm, we tackle the stochastic scheme approximating the diffusion by proper Markov chains in a finite dimensional space. To illustrate our approach, we consider a marketing problem based on a real data set. In particular, we study the relevance of the stochasticity in the optimal policies of the considered players and the impact of the number of players on the obtained solutions.Finally, we compare the solutions returned by RaBVIt-SG with the real observations and with the solution returned by RaBVItG considering a deterministic version of the game

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