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Issue title: Engineering and Management of IDTs for Knowledge Management Systems
Guest editors: Leonardo Garridow, Francisco Cervantes-Pérezx, Cleotilde Gonzálezy and Manuel Moraz
Article type: Research Article
Authors: O'Leary, Daniel E.
Affiliations: Marshall School of Business, University of Southern California. Los Angeles, CA 90089-0441, USA. Tel.: +1 213 740 4856; Fax: +1 213 747 2815; E-mail: oleary@usc.edu | [w] Center for Intelligent Computing and Robotics, Monterrey Tech, Mexico | [x] CCADET, Universidad Nacional Autónoma de México, México | [y] Social and Decision Sciences Department, Carnegie-Mellon University, USA | [z] Information Systems Department Autonomous University of Aguascalientes, Mexico
Abstract: Much of knowledge management has been aimed at capturing, converting and connecting information and knowledge as it is generated in an organization. As a result, knowledge management is focused on the past and present, providing decision makers information and knowledge. Decision makers are then responsible for using that knowledge to anticipate the future. As a result, knowledge management systems generally do not have a capability to anticipate the future. Thus, there is interest in understanding how knowledge management systems will be able to accommodate anticipation of the future at the systems level. One approach is the use of so-called "mirror worlds." The concept of a mirror world is based on a bold assertion: "You will look into a computer screen and see reality." With mirror worlds, managers could be proactive, anticipating what might happen and acting accordingly, instead of waiting till events happen and then reacting. Using transaction and other data, information and knowledge, mirror worlds of companies could be built in order to anticipate the future. This paper compares mirror worlds to other virtual worlds, evaluates the state of mirror worlds and examines potential limitations of such constructs for predictive knowledge management.
Keywords: Mirror world, knowledge management, virtual world, predictive knowledge management
DOI: 10.3233/IDT-2010-0068
Journal: Intelligent Decision Technologies, vol. 4, no. 1, pp. 39-50, 2010
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