论文标题
在离散意见动态模型中粗网的块大小依赖性：美国总统选举的应用
Block size dependence of coarse graining in discrete opinion dynamics model: Application to the US presidential elections
论文作者
论文摘要
美国总统大选的选举投票系统类似于通常用于研究物理系统中相过渡的粗糙麦片程序。在最近的一篇论文中，表现出阶段过渡的意见动力学模型被证明能够解释候选人赢得更多流行票的候选人仍可能在选举学院制度的基础上失去大选。我们探讨了这种可能性对各种因素的依赖性，例如状态数量和总人口（即系统尺寸）并获得有趣的扩展行为。与真实数据相比，这表明在模型假设中计算出的少数族裔胜利的可能性确实接近可能的最高值。此外，我们还实施了一个两步的粗栅栏程序，与意见动力学和信息理论相关。
The electoral college of voting system for the US presidential election is analogous to a coarse graining procedure commonly used to study phase transitions in physical systems. In a recent paper, opinion dynamics models manifesting a phase transition, were shown to be able to explain the cases when a candidate winning more number of popular votes could still lose the general election on the basis of the electoral college system. We explore the dependence of such possibilities on various factors like the number of states and total population (i.e., system sizes) and get an interesting scaling behavior. In comparison with the real data, it is shown that the probability of the minority win, calculated within the model assumptions, is indeed near the highest possible value. In addition, we also implement a two step coarse graining procedure, relevant for both opinion dynamics and information theory.
