Ubiquitous Computing and Knowledge Access

As computers are embedded in all devices, from pens to microwaves to walls, the spaces around us will become intelligent (Nixon, Lacey, and Dobson 1999; Thomas and Gellersen 2000). Intelligent spaces are generally characterized by the potential for ubiquitous access to information, people, and artificial agents, and the provision of information among potentially unbounded networks of agents (Kurzweil 1988). The general claim is that ubiquitous computing will enable everyone to have access to all information all the time. In such an environment, it is assumed that inequities will decrease. This is unlikely. While ubiquitous computing will enable more people to access more information more of the time, there will still be, short of major reforms, people with little to no access to computing. There will be excess information available, information making it difficult to discern true from false information. There will be barriers in access to information based on legislation, learning, and organizational boundaries. While information will diffuse faster, the likelihood of consensus being reached and being accurate given the information will depend on a variety of other factors such as group size, the complexity of the task and associated knowledge, initial distribution of information in the group, and do on. As a result, things may move faster, but not necessarily better.

Initial simulation results suggest that even when there are advanced IT capabilities, there will still be pockets of ignorance, certain classes of individuals will have privileged access to information and the benefits and power that derive from that, groups will need to share less information to be as or more effective, databases may decrease shared knowledge and guarantee information loss, and smaller groups will be able to perform as well or better than larger groups (Alstyne, M. v., and Brynjolfsson, E. 1996; Carley 1999). To address issues such as these, researchers are beginning to use multiagent network models. These models draw on research on social and organizational networks (Nohira and Eccles 1992), advances in network methodology (Wasserman and Faust 1994), and complex system models such as multiagent systems (Lomi and Larsen 2001). In these models, the agents are constrained and enabled by their position in the social, organizational, and knowledge networks. These networks influence who interacts with whom. As the agents interact, they learn, which in turn changes with whom they interact. The underlying networks are thus dynamic. The results suggest that organizations of the future might be flatter, with individuals coming and going from teams based on skills, that is, what they know, and not whom they know. As a result, social life will become more divorced from organizational life. Initial simulation results suggest that if information moves fast enough, decisions will become based not as much on information as on the beliefs of others; this should be particularly true of strategic decisions.

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