Estimating the Size of Hard-to-Count Populations

社会在很大程度上依赖标准的人口普查和调查方法来确定可被归类为某些可定义的亚群的人数。然而，有许多亚群不能通过直接统计来统计，例如被强奸的妇女人数、酗酒或吸毒人数、离家出走的儿童人数或特别容易感染某些疾病的人数。遇到难以统计的情况的频率越来越高，极大地提高了人们对寻找估计这类集合的大小的概率方法的兴趣，本项目为这一问题提供了一种新颖而有希望的方法。研究团队由一名人类学家、一名物理学家和一名数学家组成，他们成功地合作了多年--最近合作开发了一种社交网络分析形式，根据网络规模的数学模型，将相对较小规模的个人熟人数据投影到最小和最大界限中。今后两年的活动将侧重于改进该模型的理论基础，通过对现有数据的重新分析来测试和完善该模型，并开展进一步的实地工作，以探索不同数据收集技术的效果。预期的结果应该会大大增加我们对社交网络现象的了解，或许还会为开发一系列能够预测不同类型难以统计的人口规模的模型开辟道路。

Society has come to rely heavily on standard census and survey methods for determining the number of individuals who may be classified as belonging to some definable subpopulation. However there are many subpopulations that cannot be enumerated by direct count, such as the number of women who have been raped, the number of alcohol or drug abusers, the number of runaway children, or the number people who are particularly susceptible to certain diseases. The increasing frequency with which hard-to- count situations are being encountered has greatly heightened interest in finding probabilistic methods for estimating the size of such sets, and the present project offers an original and promising approach to the problem. The research team consists of an anthropologist, a physicist, and a mathematician who have successfully collaborated for many years--most recently in developing a form of social network analysis in which relatively small-scale personal acquaintanceship data are projected into minimum and maximum boundaries according to a mathematical model of network size. Activities over the next two years will focus on improving the theoretical basis for the model, testing and refining it through reanalysis of existing data, and performing further fieldwork aimed at exploring the effects of different techniques of data collection. The anticipated results should considerably increase our knowledge of social network phenomena, and perhaps open the way for developing a family of models capable of predicting the size of different kinds of hard-to- count populations.