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.

中文翻译：

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加强企业网络中知识转移的干预情景

我们研究了研发网络中公司的多代理模型。每家公司的特征在于其知识存量 $x_{i}(t)$，它遵循非线性动态。它可以随着来自其他公司的投入而增长，即通过知识转移，否则就会衰减。维持交互是昂贵的。如果预期的知识增长没有实现，企业可以离开网络，这可能导致其他企业也离开网络。本文讨论了两种自下而上的干预方案，以防止、减少或延迟企业离开的连锁反应。第一个是基于网络可控性的形式主义，其中驱动节点被识别并随后通过降低成本来激励。第二种结合了节点干预和网络干预。它提出了对单个企业的受控移除和对离开的企业的随机替换。这允许生成小级联，从而防止发生大级联。我们发现这两种方法都成功地减轻了级联效应，从而提高了研发网络的弹性。