Welcome to Center for Computational Analysis of Social and Organizational Systems (CASOS)!
CASOS brings together computer sciences, dynamic network analysis and the empirical study of complex socio-technical systems. Computational and social network techniques are combined to develop a better understanding of the fundamental principles of organizing, coordinating, managing and destabilizing systems of intelligent adaptive agents (human and artificial) engaged in real tasks at the team, organizational or social level. Whether the research involves the development of metrics, theories, computer simulations, toolkits, or new data analysis techniques advances in computer science are combined with a deep understanding of the underlying cognitive, social, political, business and policy issues.
CASOS is a university wide center drawing on a group of world class faculty, students and research and administrative staff in multiple departments at Carnegie Mellon. CASOS fosters multi-disciplinary research in which students and faculty work with students and faculty in other universities as well as scientists and practitioners in industry and government. CASOS research leads the way in examining network dynamics and in linking social networks to other types of networks such as knowledge networks. This work has led to the development of new statistical toolkits for the collection and analysis of network data (Ora and AutoMap). Additionally, a number of validated multi-agent network models in areas as diverse as network evolution , bio-terrorism, covert networks, and organizational adaptation have been developed and used to increase our understanding of real socio-technical systems.
CASOS research spans multiple disciplines and technologies. Social networks, dynamic networks, agent based models, complex systems, link analysis, entity extraction, link extraction, anomaly detection, and machine learning are among the methodologies used by members of CASOS to tackle real world problems.