Intervention Scenarios to Enhance Knowledge Transfer in a Network of Firms

Abstract

We investigate a multi-agent model of firms in a Research & Development (R&D) network. Each firm is characterized by its knowledge stock xi(t), which follows a non-linear dynamics. xi(t) grows with the input from other firms, i.e., by knowledge transfer, and decays otherwise. However, maintaining the interactions that increase knowledge stock is costly for all partners involved. Because of this, firms leave the network whenever their expected knowledge growth is not realized. This, in turn, may cause other firms also to leave the network. The paper discusses two bottom-up intervention scenarios to prevent, reduce, or delay such cascades of firms leaving. The first one is based on the formalism of network controllability, in which driver nodes are identified and subsequently incentivized, by reducing their costs. The second one combines node interventions and network interventions. It proposes the controlled removal of a single firm and the random replacement of firms leaving. This allows to generate small cascades, which prevents the occurrence of large cascades. We find that both approaches successfully mitigate cascades and thus improve the resilience of the R&D network.