Statistical Extensions and New Applications of Cultural Consensus Theory

这项研究项目将扩展文化共识理论(CCT)的计算、统计和方法论方面，以应对各种新情况。一组个人共享的知识、偏好和信念可以在各种环境中向感兴趣的各方提供有价值的信息。例如，一次创伤事件的目击者报告，关于秘密网络成员之间关系的情报报告，一群教师给学生论文分配的分数，以及特定文化群体中共同的民间医学信仰。文化共识理论(CCT)是一种正式的统计方法，用来分析对关于共享知识的问题的反应，确定是否有证据表明存在潜在的共识，如果有，则汇集反应以揭示群体内的共享知识。这一项目将扩大CCT，以涵盖上述例子，在这些例子中，除了个人的回答之外，没有可用的基本事实来确定反映群体共识的问题的答案。估计共识知识对于科学和解决重要的国家问题至关重要。例如，适当汇集目击者的报告可以帮助警方进行调查。适当地汇集有关秘密网络的情报信息有助于发现其性质。开发更好的方法来汇集评分员的反应可以改善对学生能力的评估，发现共同的医学信念可以导致公共卫生政策，使信念和科学医学知识保持适当的一致。将为新模型开发免费软件和用户指南。这项研究项目将通过开发新的响应模型、发现其特性并增强其统计推断来扩大CCT的范围。CCT模型将针对几种不同的情况开发：配对比较(其中个人表明他们对选项对的偏好)；网络(其中个人指示网络中的哪些节点是相连的)；以及相似性、距离和三合一问卷(其中，例如，个人指示两个项目在语义记忆中有多接近或多相似)。新模型将使用统计生成的数据和真实的实验数据进行评估。

This research project will expand the computational, statistical, and methodological aspects of Cultural Consensus Theory (CCT) to handle a variety of new situations. Knowledge, preferences, and beliefs shared by a group of individuals can provide valuable information to interested parties in a variety of settings. Examples include eyewitness reports of a traumatic event, intelligence reports about the relationships between members of a covert network, grades assigned to student essays by a group of teachers, and shared folk medical beliefs in a particular cultural group. Cultural Consensus Theory (CCT) is a formal, statistical way to analyze responses to questions regarding shared knowledge, to determine if there is evidence for an underlying consensus, and if so, to pool the responses to uncover the shared knowledge within the group. This project will expand CCT to cover examples such as the ones above where there is no available ground truth to determine the answers to the questions that reflect the group consensus apart from the individuals' responses. Estimating consensus knowledge is crucial for science and for addressing important national problems. For example, properly pooled reports from eyewitnesses can assist in police investigations. Proper pooling of intelligence information about a covert network can assist in discovering its nature. Developing better ways to pool the responses of graders can improve the assessment of student ability, and uncovering shared medical beliefs can lead to public health policy that brings beliefs and scientific medical knowledge into proper alignment. Freely available software will be developed for the new models along with user guides.This research project will increase of the scope of CCT by developing new response models, discovering their properties, and augmenting their statistical inference. CCT models will be developed for a number of different cases: paired-comparisons (where individuals indicate their preferences among pairs of options); networks (where individuals indicate which nodes in the network are connected); and similarity, distance, and triad questionnaires (where, for example, individuals indicate how close or how similar two items are in semantic memory). The new models will be evaluated with statistically generated data and real experimental data.