Estimating the Size of Hard-to-Count Populations

社会在很大程度上依赖于标准的人口普查和调查方法来确定可以归类为属于某些可定义的亚人口的个人人数。然而，有许多亚群体无法通过直接计数来枚举，例如被强奸的妇女人数、酗酒或滥用药物的人数、离家出走的儿童人数或特别容易感染某些疾病的人数。难以计数的情况越来越频繁地遇到，这极大地提高了人们对寻找估计这些集合大小的概率方法的兴趣，而本项目提供了一种新颖而有前途的方法来解决这个问题。研究团队由一位人类学家、一位物理学家和一位数学家组成，他们已经成功合作多年——最近的成果是开发一种社会网络分析形式，根据网络规模的数学模型，将相对小规模的个人熟人数据投射到最小和最大边界。今后两年的活动将集中于改进模型的理论基础，通过重新分析现有数据来检验和改进模型，并进行进一步的实地工作，目的是探索不同数据收集技术的效果。预期的结果将大大增加我们对社会网络现象的认识，并可能为开发一系列能够预测各种难以计数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.