论文标题
部分可观测时空混沌系统的无模型预测
Identifying Possible Winners in Ranked Choice Voting Elections with Outstanding Ballots
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Several election districts in the US have recently moved to ranked-choice voting (RCV) to decide the results of local elections. RCV allows voters to rank their choices, and the results are computed in rounds, eliminating one candidate at a time. RCV ensures fairer elections and has been shown to increase elected representation of women and people of color. A main drawback of RCV is that the round-by-round process requires all the ballots to be tallied before the results of an election can be calculated. With increasingly large portions of ballots coming from absentee voters, RCV election outcomes are not always apparent on election night, and can take several weeks to be published, leading to a loss of trust in the electoral process from the public. In this paper, we present an algorithm for efficiently computing possible winners of RCV elections from partially known ballots and evaluate it on data from the recent New York City Primary elections. We show that our techniques allow to significantly narrow down the field of possible election winners, and in some case identify the winner as soon as election night despite a number of yet-unaccounted absentee ballots, providing more transparency in the electoral process.
