Estimating population sizes has long been of interest, from the estimation of the human or ecological population size within regions or countries to the hidden number of civilian casualties in a war. Total enumeration of the population, for example, via a census, is often infeasible or simply impractical. However, a series of partial enumerations or observations of the population is often possible. This has led to the ideas of capture-recapture methods, which have been extensively used within ecology to estimate the size of wildlife populations, with an associated measure of uncertainty, and are most effectively applied when there are multiple capture occasions. Capture-recapture ideology can be more widely applied to multiple data-sources, by the linkage of individuals across the multiple lists. This is often referred to as Multiple Systems Estimation (MSE). The MSE approach has been preferred when estimating "capture-shy" or hard-to-reach populations, including those caught up in the criminal justice system; or homeless; or trafficked; or civilian casualties of war. Motivated by a range of public policy applications of MSE, each briefly introduced, we discuss practical problems with potentially substantial methodological implications. They include: "period" definition; "case" definition; when an observed count is not a true count of the population of interest but an upper bound due to mismatched definitions; exact or probabilistic matching of "cases" across different lists; demographic or other information about the "case" which may influence capture-propensities; required permissions to access extant-lists; list-creation by research-teams or interested parties; referrals (if presence on list A results - almost surely - in presence on list B); different mathematical models leading to widely different estimated population sizes; uncertainty in estimation; computational efficiency; external validation; hypothesis-generation; and additional independent external information. Returning to our motivational applications, we focus on whether the uncertainty which qualified their estimates was sufficiently narrow to orient public policy; and, if not, what options were available and/or taken to reduce the uncertainty or to seek external validation. We also consider whether MSE was hypothesis-generating: in the sense of having spawned new lines of inquiry.

中文翻译：

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多系统估计（或捕获-重新捕获估计）为公共政策提供信息。


从估计地区或国家内的人类或生态人口规模到战争中隐藏的平民伤亡人数，估计人口规模长期以来一直受到人们的关注。例如，通过人口普查对人口进行全面统计通常是不可行的或根本不切实际的。然而，对总体进行一系列部分计数或观察通常是可能的。这催生了捕获-再捕获方法的想法，该方法已在生态学中广泛用于估计野生动物种群的规模以及相关的不确定性测量，并且在存在多个捕获场合时最有效地应用。通过跨多个列表的个人链接，捕获-重新捕获意识形态可以更广泛地应用于多个数据源。这通常称为多系统估计 (MSE)。在估计“害怕被捕”或难以接触的人群（包括那些卷入刑事司法系统的人群）时，首选 MSE 方法；或无家可归；或被贩运；或战争造成的平民伤亡。受到一系列 MSE 公共政策应用（每项都进行了简要介绍）的推动，我们讨论了具有潜在重大方法论意义的实际问题。 它们包括： “期间”的定义； “案例”定义；当观察到的计数不是感兴趣总体的真实计数而是由于定义不匹配而导致的上限时；不同列表中“案例”的精确或概率匹配；有关“案件”的人口统计或其他可能影响捕获倾向的信息；访问现有列表所需的权限；研究团队或感兴趣的各方创建列表；推荐（如果出现在列表 A 上——几乎可以肯定——出现在列表 B 上）；不同的数学模型导致估计的人口规模存在很大差异；估计的不确定性；计算效率；外部验证；假设生成；以及其他独立的外部信息。回到我们的动机应用，我们关注的是验证他们的估计的不确定性是否足够窄以指导公共政策；如果没有，可用和/或采取哪些选项来减少不确定性或寻求外部验证。我们还考虑 MSE 是否可以生成假设：从产生新的探究方向的意义上来说。