论文标题
干预方案以增强公司网络中的知识转移
Intervention scenarios to enhance knowledge transfer in a network of firm
论文作者
论文摘要
我们研究了R \＆D网络中公司的多代理模型。每个公司的特征是其知识库存$ x_ {i}（t）$，该$遵循非线性动力学。它可以随着其他公司的投入，即通过知识转移而增长，并衰减。保持互动是昂贵的。如果没有实现其预期知识增长，则可以离开网络，这可能会导致其他公司也离开网络。该论文讨论了两种自下而上的干预方案，以防止，减少或延迟公司离开的级联。第一个是基于网络可控性的形式主义，其中通过降低成本来识别并随后激励驱动器节点。第二个结合了节点干预措施和网络干预措施。它提出了受控的单个公司的控制，并随机更换离开的公司。这允许产生小的级联反应，从而防止发生大型级联反应。我们发现两种方法都成功地减轻了级联反应，从而提高了R \＆D网络的弹性。
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.
