A Network Approach For Interpreting Social Landscapes

在许多方面，人类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会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——经常被用来在社交网络中宣传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来区分自己和他人，或者更紧密地将自己与他人认同。考古学具有独特的条件，通过对人工制品风格的研究，可以在广阔的空间和时间范围内追踪社会网络的发展。在这个更广泛的背景下，北卡罗来纳大学格林斯博罗分校的查尔斯·埃格兰博士（Charles Egeland）将与几位同事一起开发一种基于人工智能的方法，以识别人工制品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，这些插件可以生成考古遗址之间社会联系模式和强度的可视化、基于图形的表示。两名研究生将接受培训，在社会科学背景下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由其他用户免费在线访问，该技术最终可以应用于任何由2D数字图像表示的对象。最终，这个项目将有助于弄清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会网络，并在快速变化的环境中导航。研究人员将带领一个由考古学家、古气候学家和计算机科学家组成的团队：(1)建立一个包含约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web的应用程序，该应用程序使用人工智能来识别人工制品数字表示中的风格模式；(3)为开源图形分析平台开发定制插件，以生成多尺度的可视化表示和定量描述符。有了这些数据，研究小组将探索地理、环境不确定性和人口密度如何影响人们如何构建和使用他们的社会网络来调节社会边界和抵消资源短缺。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.