论文标题
部分可观测时空混沌系统的无模型预测
Voting From Jail
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。
We leverage new data on daily individual-level jail records and exploit the timing of incarceration to estimate the causal effects of jail incarceration on voting from jail in 2020. We find that registered voters booked into county jails for the full duration of 2020 voting days were on average 46% less likely to vote in 2020, relative to registered voters booked into the same jails within 7-42 days after Election Day. The estimated negative effect of incarceration on voting from jail was much larger for Black registered voters, who were 78% less likely to vote in 2020 if booked into county jails for the full duration of 2020 voting days, relative to Black registered voters booked into the same jails just after Election Day. Placebo tests indicate no effects of 2020 jail incarceration on the 2012 or 2016 turnout of registered voters. We find inconsistent effects of jail incarceration on voter registration in 2020, and effect sizes of comparable magnitude for turnout unconditional on registration status. Our findings reveal the pressing need to enable voting-eligible incarcerated individuals to exercise their constitutional right to vote, and to address troubling racial disparities in the effect of jail incarceration on the exercise of the right to vote.
