论文标题
通过模拟退火量化搬运工
Quantifying Gerrymandering With Simulated Annealing
论文作者
论文摘要
Gerrymandering是基于对投票区域边界的操纵的选举的变态，并且是历史上重要但很难进行分析的任务。我们提出了一个马尔可夫链蒙特卡洛（Monte Carlo），并带有模拟退火，作为衡量区域计划不公平程度的解决方案。我们使用模拟退火的重新划分马尔可夫链的实施，成功地应用了德克萨斯州的重新分配链，以成功地应用重新划分链，以产生加速的结果。这证明了有力的证据表明，模拟退火可以有效地为大选迅速产生代表性投票分配，并且能够表明在颁布的地区计划中表明不公平的偏见。
Gerrymandering is the perversion of an election based on manipulation of voting district boundaries, and has been a historically important yet difficult task to analytically prove. We propose a Markov Chain Monte Carlo with Simulated Annealing as a solution for measuring the extent to which a districting plan is unfair. We put forth promising results in the successful application of redistricting chains for the state of Texas, using an implementation of a redistricting Markov Chain with Simulated Annealing to produce accelerated results. This demonstrates strong evidence that Simulated Annealing is effective in quickly generating representative voting distributions for large elections, and furthermore capable of indicating unfair bias in enacted districting plans.
