论文标题
加强随机过程的网络：渐近极化的概率和相关的一般结果
Networks of reinforced stochastic processes: probability of asymptotic polarization and related general results
论文作者
论文摘要
在加强随机过程的网络中，对于参数的某些值，所有代理的倾斜度都同步并肯定地趋向于某个随机变量。目前的工作旨在阐明何时可以渐近地极化代理，即，何时公共极限倾斜度可以采用极端值0或1，概率为零，严格为正或等于一个。此外，我们提出了一种合适的技术来估计这种概率，与理论结果一起，在一类Martingales在[0，1]中的一个类别和遵循特定动力学的更通用环境中构建了构图。
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 to estimate this probability that, along with the theoretical results, has been framed in the more general setting of a class of martingales taking values in [0, 1] and following a specific dynamics.
