  We investigate a multi-agent model of firms in an R\&D network. Each firm is
characterized by its knowledge stock $x_{i}(t)$, which follows a non-linear
dynamics. It can grow with the input from other firms, i.e., by knowledge
transfer, and decays otherwise. Maintaining interactions is costly. Firms can
leave the network if their expected knowledge growth is not realized, which may
cause other firms to also leave the network. The paper discusses two bottom-up
intervention scenarios to prevent, reduce, or delay 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.
