Intervention scenarios to enhance knowledge transfer in a network of firm

TL;DR

This paper models an R&D network of firms to explore intervention strategies that prevent large cascades of firm exits, enhancing network resilience through targeted control and replacement interventions.

Contribution

It introduces two novel intervention strategies based on network controllability and combined node/network interventions to mitigate cascade failures in R&D networks.

Findings

Both intervention methods effectively reduce cascade sizes.

Targeted incentivization of driver nodes enhances network stability.

Replacing leaving firms with new ones prevents large cascades.

Abstract

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.