Statistical Extensions and New Applications of Cultural Consensus Theory

这个研究项目将扩大文化共识理论（CCT）的计算，统计和方法方面，以处理各种新情况。 一群人共享的知识、偏好和信念可以为各种环境中的利益相关方提供有价值的信息。 例子包括目击者对创伤事件的报告，关于秘密网络成员之间关系的情报报告，一组教师对学生论文的评分，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，用于分析对共享知识问题的回答，以确定是否存在潜在共识的证据，如果是，则将回答汇总以揭示组内的共享知识。 本项目将扩展CCT，以涵盖上述例子，其中没有可用的基本事实来确定问题的答案，这些问题反映了除了个人的回答之外的群体共识。 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，适当收集目击者的报告有助于警方调查。 适当汇集关于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，揭示共同的医学信仰可以导致公共卫生政策，使信仰和科学的医学知识保持适当的一致。 免费提供的软件将开发新的模型沿着用户guides.This研究项目将增加CCT的范围，通过开发新的响应模型，发现他们的属性，并增加他们的统计推断。 CCT模型将被开发用于许多不同的情况：配对比较（其中个体表明他们在成对选项中的偏好）;网络（其中个体表明网络中的哪些节点是连接的）;以及相似性、距离和三元组问卷（例如，个体表明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.