社会强化学习如何导致亚稳态极化与选民模型
How social reinforcement learning can lead to metastable polarisation and the voter model
AI总结：
本文指出基于社会强化学习的意见极化实为亚稳态且渐近达成共识，强调模型遍历性等性质决定分析工具的选择，不能仅靠仿真判断。
摘要：
以往对意见极化持续性的解释通常包含使极化成为可能的建模假设（即允许一对智能体在意见上产生分歧的假设，如排斥性交互或有界信任）。一个例外是最近的一项仿真研究，该研究表明当智能体使用社会强化学习形成意见时，极化是持续存在的。我们的目标是强调强化学习在意见动力学建模背景下的实用性，但在选择用于研究此类模型的工具时需要谨慎。我们表明，在该仿真研究的模型中观察到的极化无法无限期持续，并且渐近地以概率一达成共识。通过在强化学习模型与选民模型之间建立联系，我们论证了所观察到的极化是亚稳态的。最后，我们表明，对智能体的学习过程进行轻微修改会将模型从非遍历性转变为遍历性。我们的结果表明，强化学习可能是对意见动力学中的极化进行建模的强大方法，但适用于分析此类模型的工具（例如用于研究的对象，如平稳分布或吸收时间）关键取决于它们的性质（如遍历性或瞬态）。这些性质由学习过程的细节决定，并且可能难以仅基于仿真来识别。
英文摘要：
Previous explanations for the persistence of polarization of opinions have typically included modelling assumptions that predispose the possibility of polarization (i.e., assumptions allowing a pair of agents to drift apart in their opinion such as repulsive interactions or bounded confidence). An exception is a recent simulation study showing that polarization is persistent when agents form their opinions using social reinforcement learning. Our goal is to highlight the usefulness of reinforcement learning in the context of modeling opinion dynamics, but that caution is required when selecting the tools used to study such a model. We show that the polarization observed in the model of the simulation study cannot persist indefinitely, and 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 (objects to study such as the stationary distribution, or time to absorption for example) appropriate for analysing such models crucially depend on their properties (such as ergodicity, or transience). These properties are determined by the details of the learning process and may be difficult to identify based solely on simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
