Previous explanations for the persistence of polarization of opinions have typically included modelling assumptions that predispose the possibility of polarization (e.g.\ repulsive interactions). An exception is recent research showing that polarization is stable when agents form their opinions using reinforcement learning. We show that the polarization observed in this model is not stable, but exhibits consensus asymptotically with probability one. By constructing a link between the reinforcement learning model and the voter model, we argue that the observed polarization is metastable. Finally, we show that a slight modification in the learning process of the agents changes the model from being non-ergodic to being ergodic. Our results show that reinforcement learning may be a powerful method for modelling polarization in opinion dynamics, but that the tools appropriate for analysing such models crucially depend on the properties of the resulting systems. Properties which are determined by the details of the learning process.

翻译：先前关于观点极化持续存在的解释通常包含倾向于极化可能性的建模假设（例如排斥性相互作用）。一个例外是近期研究表明，当智能体使用强化学习形成观点时，极化是稳定的。我们证明该模型中观察到的极化并非稳定状态，而是以概率一渐近趋于共识。通过建立强化学习模型与选民模型之间的关联，我们认为观察到的极化处于亚稳态。最后，我们证明对智能体学习过程的微小修改会使模型从非遍历性转变为遍历性。我们的研究结果表明，强化学习可能是建模观点动力学中极化现象的有效方法，但分析此类模型所需工具的关键取决于所得系统的性质——这些性质由学习过程的具体细节决定。