A Network Approach For Interpreting Social Landscapes

人类在许多方面都是由他们与他人的社会互动来定义的。事实上，人类适应系统的核心在于人们利用社交网络来促进解决资源短缺、信息获取和传播、政治动荡和冲突解决等集体问题。物质文化服装、艺术品、珠宝经常被用来宣传一个人在社交网络中的个人和群体身份的重要信息。人们这样做的一种方式是采用或操纵一个对象的风格，以使自己与他人区分开来，或者更紧密地将自己与他人认同。考古学是唯一的装备，通过研究人工制品的风格，跟踪社交网络的发展跨越广阔的空间和时间。在这一更广泛的背景下，格林斯伯勒的北卡罗来纳州大学的查尔斯·埃格兰博士沿着几位同事将开发一种基于人工智能的方法，以识别文物数字图像之间的风格相似性。文物之间的风格相似程度将被输入到定制编码的程序插件中，这些插件可以产生考古遗址之间社会联系模式和强度的视觉，基于图形的表示。两名研究生将接受培训，在社会科学环境中应用人工智能和软件集成。该项目生成的数据、人工智能方法和插件将可供其他用户在线免费访问，该技术最终可应用于由2D数字图像表示的任何对象。最终，该项目将有助于阐明人类如何构建和使用社交网络来应对挑战。埃格兰博士和他的研究小组将研究旧石器时代的人们如何利用物质文化来构建社交网络，并在迅速变化的环境中导航。研究人员将领导一个由考古学家、古气候学家和计算机科学家组成的团队，（1）收集一个包含约200幅雕刻文物数字图像的数据库;（2）构建一个开放访问的基于网络的应用程序，该应用程序使用人工智能来识别文物数字表示中的风格模式;以及（3）为开源图形分析平台开发定制插件，以产生多个尺度的可视化表示和定量描述符。利用这些数据，研究团队将探索地理、环境不确定性和人口密度如何影响人们构建和使用社交网络来规范社会边界和抵消资源短缺的方式。该奖项反映了NSF的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Humans are in many ways defined by their social interactions with others. Indeed, the heart of human adaptive systems lies in peoples’ use of social networks to facilitate solutions to collective problems like resource shortfalls, information acquisition and dissemination, political turmoil, and conflict resolution. Material culture—clothing, artwork, jewelry—is often used to advertise important information about one’s personal and group identity within a social network. One way in which people do this is to adopt or manipulate the style of an object to distinguish themselves from others or more closely identify themselves with others. Archaeology is uniquely equipped, through the study of artifact style, to track the development of social networks across vast expanses of space and time. Within this broader context, Dr. Charles Egeland of the University of North Carolina at Greensboro, along with several colleagues, will develop a methodology based on artificial intelligence to identify stylistic similarities among digital images of artifacts. The level of stylistic similarity among artifacts will be input into custom-coded program plugins that can produce visual, graph-based representations of the pattern and strength of social ties between archaeological sites. Two graduate students will be trained to apply artificial intelligence and software integration in a social science setting. The data, artificial intelligence methodology, and plugins generated by this project will be freely accessible online by other users, and the technique can eventually be applied to any object represented by a 2D digital image. Ultimately, this project will help clarify how humans construct and use social networks to meet challenges. Dr. Egeland and his research team will examine how Paleolithic people used material culture to construct social networks and navigate the rapidly changing environments. The researcher will lead a team of archaeologists, paleoclimatologists, and computer scientists to (1) assemble a database of ~200 digital images of engraved artifacts; (2) construct an open-access, web-based application that uses artificial intelligence to identify stylistic patterns among the digital representations of the artifacts; and (3) develop custom plugins for an open-source graph analysis platform to produce visual representations of, and quantitative descriptors at multiple scales. With these data, the research team will explore how geography, environmental uncertainty, and population density influenced how people constructed and used their social networks to regulate social boundaries and offset resource shortages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.