Statistical Extensions and New Applications of Cultural Consensus Theory

University Of California-Irvine, Irvine CA

Investigators

Abstract

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.