Knowledge Engines for Critical Decision Support

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. Knowledge management (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 twofold: (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.