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.
Highlights
Interventions belong to the tool box of systems design [1]
To determine the driver nodes, we choose two different approaches: (a) we identify all firms that belong to the 20% with the highest knowledge stock xistat, and (b) we instead identify all firms that belong to the 20% with the highest control contribution Ci, a measure for the ability of firms to influence others, as described below
According to the network intervention, they are replaced by new firms that randomly connect to the network
Summary
Interventions belong to the tool box of systems design [1]. The ability to influence systems such that they reach a desired state or show a desired behavior, is of relevance for engineers and operators. Most of the interventions in economic systems are targeted at the macro level, for instance by adjusting tax rates or legal conditions. They follow a top-down approach: a centralized decision to change some “boundary conditions” induces an adaptation of the system, hopefully in the right, i.e., wanted, direction. This approach is contrasted with the bottom-up approach that targets system elements rather than systems as a whole [2,3,4]. In socio-economic systems, agent specific interventions include, for example, monetary incentives (e.g., reduced costs, bonuses) or privileged access to resources (e.g., information, credit) [6, 7]
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