Statistical Extensions and New Applications of Cultural Consensus Theory

本研究计划将扩展文化共识理论（CCT）的计算、统计和方法论方面，以处理各种新情况。一群人共享的知识、偏好和信念可以在各种情况下为相关方提供有价值的信息。例子包括目击证人对创伤性事件的报告、秘密网络成员之间关系的情报报告、一组老师给学生论文分配的分数，以及在特定文化群体中共享的民间医学信仰。文化共识理论（CCT）是一种正式的统计方法，用于分析对有关共享知识的问题的回答，以确定是否存在潜在共识的证据，如果存在，则汇集回答以揭示群体内的共享知识。这个项目将扩大有条件现金交易，以涵盖诸如上述的例子，在这些例子中，除了个人的回答之外，没有可用的基础真理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协助警方调查。适当汇集有关秘密网络的情报信息有助于发现其本质。开发更好的方法来汇集评分者的反应可以改善对学生能力的评估，揭示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适当地结合起来。将为新型号开发免费软件以及用户指南。本研究项目将通过开发新的响应模型，发现其性质，并增强其统计推断来扩大CCT的范围。CCT模型将针对许多不同的情况开发：配对比较（个人在配对选项中表明其偏好）；网络（个人指出网络中的哪些节点是连接的）；以及相似性、距离和三位一体问卷（例如，个人指出两个项目在语义记忆中的接近程度或相似程度）。新模型将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.