Modeling Skill Growth and Decay in Edge Organizations: Near-Optimizing Knowledge and Power Flows (Phase Two)
STANFORD UNIV CA TERMAN ENGINEERING CENTER
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This paper outlines efforts to model, simulate and ultimately optimize knowledge flows in Edge organizations. We begin by reviewing Phase I research which explored how knowledge inventory flows through organizations, analogously to perishable, physical goods inventory in a supply chain, and uncovered useful insights to clarify current understanding and permit initial quantification of knowledge management impacts on organizational performance. Current Phase II efforts are then described that classify, quantitatively model, and simulate knowledge flows within and among individuals in Edge organizations. Empirical, experimental data on rates of learning and forgetting drawn from the social and cognitive psychology literature provide the basis for defining and modeling agent learning and forgetting micro-behaviors in our POW-ER computational simulation model of organizations. Phase II micro-level skill acquisition builds on Phase I macro-level inventory control by modeling the trajectories of individual knowledge flows associated with dynamic knowledge inventory increases and decreases. Using this model, we conduct intellective experiments using models of idealized work processes and organizations and emulation experiments to replicate outcomes of real work processes and organizations for model refinement and validation. The goal of these experiments is to determine organizationally, contingently optimal knowledge intervention strategies. Cumulative Phase III efforts are introduced that integrate findings from prior phases to engineer knowledge management solutions in organizations via a Knowledge Chain Management approach.
- Information Science