Cohesion and Organization Through Networked Computation in Overly Risky and Distinctly Isolated Areas
Abstract
Cohesion and Organization through Networked Computation in Overly Risky and Distinctly Isolated Areas (CONCORDIA) was an attempt to develop a set of algorithms for assessing team cohesion via wearable devices collecting physiological data from team members while they performed tasks designed specifically to assess team cohesion - COHESION and CubeCrusher. The algorithms utilized a variety of machine learning techniques to identify when a team members behavior started to deviate from that of the rest of their team, as well as to predict performance metrics. The results, while not conclusive, indicate that there is a significant probability that algorithms with a high level of accuracy and robustness can be developed. We also developed, in concurrence with the machine learning algorithms, a prototype interface for displaying team cohesion in real time according to physiological data. We provide suggestions for future research directions that could make such a system effective.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2024
- Accession Number
- AD1221027
Entities
People
- Jeremy Gottlieb
- Joan Zheng
- Nathanael L. Keiser
- Noam Benkler
- Pete Roma
Organizations
- Smart Information Flow Technologies