Computational analysis is significantly impacting the way groups, organizations, and societies are managed and organizational decisions and policies are evaluated. The combination of computational analysis and social science is re-shaping the way we think about groups, organization and society. It is leading to new methods for analyzing vast quantities of data to support informed decision making and better prediction. The key to this area is the application of multiple methods and techniques such as social network analysis, agent based simulation, network visualization and machine learning to complex social problems, and the development of new inter-disciplinary techniques.

Groups, organizations, and societies are inherently computational and computational multi-agent systems are inherently organizational. Thus, within CASOS we attempt to understand and formally model two distinct but complimentary types of phenomena. The first is the natural or human group, organizational or society, which is universally formatted and continually acquires, manipulates, and produces information (and possibly other material goods) through the joint, and interlocked activities of people and automated information technologies. The second is the artificial computational systems which is generally comprised of multiple distributed agents who can mutually influence, constrain and support each other as they try to manage and manipulate the knowledge, communication and interaction networks in which they are embedded. Computational analysis is used to develop a better understanding of the fundamental principles of organizing, coordinating, and managing multiple information processing agents (whether they are human, WebBot, or robots) and the fundamental dynamic nature of groups, organizations and societies.

Within CASOS, computational analysis is not simply in service to organizational and social theorizing; rather, computational theorizing about these human phenomena is actually pushing the research envelope in terms of computational tools and techniques. CASOS research makes contributions to mainstream artificial intelligence and computer science by fostering progress on such issues as: large scale qualitative simulation, comparison and extension of optimization procedures (particularly procedures suited to extremely complex and possibly changing performance surfaces); aggregation and disaggregation of distributed objects; coordination algorithms; organizational and multiagent learning; and understanding and managing the trade off between agent quantity and computational complexity.

Research in this area requires further development of the scientific infrastructure including developing the following: easy-to-use cost-effective computational tool kits for designing and building computational models of organizations, teams, and social systems (e.g., a multi-agent oriented language with built in task objects and communication); multi-agent logics; intelligent tools for analyzing computational models; validation procedures, protocols, and canonical data sets and task descriptions; managerial decision aids based on computational organizational models; and protocols and standards for inter-agent communication.

Key concerns in this area center around determining the following items: what coordinational structures are best for what types of agents (human, robots, or WebBot) and tasks; the advantages and disadvantages of using hybrid models (such as a joint annealer and genetic programming model) for exploring organizational issues and for locating new organizational designs; representations for, and management of, uncertainty in organizational systems; the interactions among, and the relative disadvantages of various types of adaptation, evolution, learning, and flexibility; measures of organizational design; the existence of, or limitations of, fundamental principles of organizing the trade offs for system performance of task-based, agent-based, and structure-based coordination schemes; representations for information and communication technology in computational models; and the relation between distributed semantics, knowledge and metal models on teamwork, organizational culture and performance.

At Carnegie Mellon:

CASOS aims to foster multidisciplinary research on high impact projects in a number of ways:

More than 12 faculty and 9 students from 5 colleges are working in this area.

Researchers in this area are working to:

  1. Develop new concepts, theories, and knowledge about organizing and organization, coordination, adaptation, and evolution.
  2. Develop, test and deploy new network science algorithms and netowrk visualization techniques for use with both social network data, social media data, and other forms of network data.
  3. Develop tools and procedures for the validation and analysis of computational models of distributed agent systems at the group, organization, and social level.
  4. Develop simulation and network based tools and metrics that can be used in the workplace to better manage the interlocked activities of people and intelligent technologies at the interaction and knowledge level.

CASOS areas of specialization: