In a network of reinforced stochastic processes, for certain values of the parameters, all the agents' inclinations synchronize and converge almost surely toward a certain random variable. The present work aims at clarifying when the agents can asymptotically polarize, i.e. when the common limit inclination can take the extreme values, 0 or 1, with probability zero, strictly positive, or equal to one. Moreover, we present a suitable technique in order to estimate this probability that, along with the theoretical results, has been framed in the general setting of a class of martingales taking values in [0,1].

翻译：在强化随机过程网络中，对于某些参数取值，所有智能体的倾向几乎必然同步收敛至某个随机变量。本文旨在阐明智能体何时能实现渐近极化，即共同极限倾向取极端值0或1的概率为零、严格为正或等于1的情形。此外，我们提出一种适用于估计该概率的技术方法，该方法与理论结果一同被纳入取值于[0,1]的鞅类一般性框架中。