Knowledge Engines for Critical Decision Support

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Author(s): Richard M. Adler (DecisionPath, Inc., USA)
Copyright: 2008
Pages: 27
Source title: Knowledge Management Strategies: A Handbook of Applied Technologies
Source Author(s)/Editor(s): Miltiadis D. Lytras (Effat University, Saudi Arabia), Meir Russ (University of Wisconsin - Green Bay, USA), Ronald Maier (Leopold-Franzens-University of Innsbruck, School of Management Information Systems, Austria)and Ambjörn Naeve (Royal Institute of Technology, Sweden)
DOI: 10.4018/978-1-59904-603-7.ch005

Keywords: Information Science Reference / Knowledge Management / Library & Information Science

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Abstract

Current knowledge capture and retention techniques tend to codify “what-is” and “who knows” more effectively than “how-to”. Unfortunately, “how-to” knowledge is more directly actionable, and indispensable for critical organizational activities such as strategic analysis and decision-making. KM theorists often despair over “how-to” expertise as a form of tacit knowledge that is difficult to articulate, much less transfer. We argue that tacit strategic performance-based knowledge can often be captured and deployed effectively, via frameworks that combine scenario planning methods with “what-if” simulation. The key challenges are two-fold: (1) modeling complex situational contexts, including known behavioral dynamics; and (2) enabling knowledge workers to manipulate such models interactively, to safely practice situational analysis and decision-making and learn from virtual rather real mistakes. We illustrate our approach with example knowledge-based decision support solutions and provide pointers to related literature.

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