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

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. Introduction and Motivation Edge organizations [1] can only achieve their putative effectiveness through the thoughtful management of knowledge. For instance, Alberts and Hayes implement the term agility to encompass the facets of robustness, resilience, responsiveness, flexibility, innovation, and adaptation. For each of these Edge-like qualities to exist, the flows of knowledge among individuals and its contextual deployment to support shared awareness and self-synchronization in Edge organizations must first be explored, understood and managed. Toward this goal, our efforts are offered in Phases I, II, and III. Phase I explored knowledge inventory [34]. In this phase we considered knowledge as a perishable set of skills like physical goods. Using proven mathematical management science formulae and methods such as Economic Order Quantity (EOQ) and cost analysis, as well as inventory doctrines of Just-In-Case (JIC), Just-In-Time (JIT), and make vs. buy decisions, we examined knowledge flows using the metaphor perishable inventory with some success. We closed by introducing the novel concept knowledge chain management via theoretical modeling. In Phase II we refine this effort through research described in the present article by considering cognitive learning and forgetting rates. We model and test mechanisms for changing the level of participants’ knowledge by collating available experimental data in the social and psychological literature, and by observing knowledge workers in Edge organizations. This effort more precisely informs our knowledge of growth and decay, and can be used within the Knowledge Inventory modeling of Phase I. Phase III then looks forward by determining contingently optimal knowledge flows in different organizational contexts through developing a more precise methodology for Knowledge Chain Management. Background – Phase I Review A large body of research exists on information flow in organizations, going back to the pioneering work of Herbert Simon in the 1950’s [53]. However, the corresponding literature on the flow of knowledge in organizations is only just emerging (e.g. [33, 41, 44]) and remains inchoate. To gain theoretical insight into knowledge (and therefore power) flows, we began our research efforts in Phase I by describing knowledge as a set of skills that grow and decay over time due to different environmental effects. We sought to understand how such skills can be managed to maintain efficiencies required for edge-like qualities such as agility and robustness. Specifically, as noted above, we posited a model of knowledge as perishable inventory, whereby we