Modeling Skill Growth and Decay in Edge Organizations: Near-Optimizing Knowledge & Power Flows (Phase Two)

Abstract

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 knowlesge 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 knowlesge 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 organizatins. 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 (to replicate outcomes of real work processes and organizations) for model refinement and validation. The goal of these experiments is to determine organizational, contingently optimal knowledge intervention strategiees. Cumulativ Phase III efforts are introduced that integrate findings from prior phases to "engineer" knowledge management solutions in organizations via a Knowledge Chain Management approach.