The past decade has seen a tremendous increase in both the breadth and the
complexity of computational systems society has come to rely on. This
increase in turn is giving rise to a number of new and challenging societal,
management and policy issues, which themselves often call for new
technological innovations. Examples include privacy rights management, data
privacy, electronic market mechanisms and automated negotiation, dynamic
network modeling, online dispute resolution, etc. Attacking these new
problems requires profound understanding of computation and the interplay
between the managerial, personal and policy networks in which technology
operates. Unfortunately, current degree programs in traditional disciplines
(e.g. computer science, policy or management) fail to provide the kind of
multi-disciplinary curriculum needed to train tomorrow’s leaders in this
emerging area. Today’s demand for integrated expertise far exceeds supply.
As demand for this new breed of researchers continues to grow, it becomes
increasingly important to offer a PhD program that fills the void.
There is a general lack of understanding by computer scientists of social,
economic and policy issues impacted by computational systems. Yet,
increasingly more and more ACM and IEEE computer science conferences and
journals, as well as traditional funding sources, focus on work that
integrates these disciplines. The Privacy in D.A.T.A. workshop held at
Carnegie Mellon University in March 2003 brought together some of the world’s leading computer
science theorists to examine data privacy problems; the biggest hurdle was
helping these computer scientists understand the personal, organizational
and policy settings in which well-defined theoretical computer
science problems related to data privacy
exist. Similarly, multi-agent research has increasingly had to combine
methods of social and economic science with computer science, and,
conversely, social and economic sciences are increasingly turning to
multi-agent modeling for solutions to problems that elude traditional
analytical methods. Dynamic network analysis, multi-agent systems, market
mechanisms and privacy-preserving data mining, to name just a few, have
become major themes at ACM, IEEE, and AAAI conferences. Yet, while computer
science researchers are increasingly asked to address or integrate social,
economic or legal dimensions into the emerging technologies they develop,
traditional doctoral programs continue to emphasize computation as a
standalone discipline and ignore its many social, economic and policy
ramifications. In contrast, the PhD program in Computation, Organizations
and Society (COS) is a omputer science based cross-disciplinary program
that aims to train computer scientists to
understand the bigger picture in which computation operates and to create
technology from this broader vantage point.
