Knowledge Networks and Resilience: Under Which Conditions Regional Economies Overcome External Shocks

In recent years, due to the increasing instability of economic systems, the debate among scholars and policymakers about restoring economic growth has centred around the concept of resilience, that is, the ability of a system to anticipate, prepare for, respond to, and recover from a shock. This work aims to contribute to this debate by developing an agent-based model that explores the conditions under which economic systems successfully cope with external shocks and achieve a long-term growth path. The focus is on the role of network structures in regional economic resilience. The findings show that the network structures that best support long-term regional growth in stable conditions are not necessarily those that promote resilience and recovery in the aftermath of a shock. In stable environments, or when non-specialized regions experience only mild disruptions, scale-free networks perform best, followed by small-world structures. The presence of highly connected hubs in scale-free networks enhances knowledge diffusion and cumulative learning, while the combination of clustering and short average path lengths in small-world networks also supports growth. However, when a shock hits a specialized regional economy, the ranking changes. In the immediate aftermath of the shock, more random and open network structures facilitate faster adjustment, whereas in the medium run scale-free networks regain their advantage. In such contexts, openness and access to diverse knowledge sources become critical for adaptation and recovery.