n this paper, we construct the foundation for a new theory for the management of knowledge intensive systems or organizations termed knowledge supernetworks. The framework allows for explicit multicriteria decision-making and determines the optimal flows. We develop the fundamental knowledge supernetwork model with fixed demands and present concrete applications to a news organization, an intelligence agency, and a global financial institution. We then propose model extensions with elastic demands. The formulations of the governing optimality/equilibrium conditions are given as variational inequality problems. Qualitative properties of the solution patterns are provided along with algorithms that exploit the network structure of the problem and enhance the operationalism of the framework. Finally, numerical examples are provided to illustrate both the generality of the knowledge supernetwork concept and theory as well as the computational procedures.