Natural Disasters, Cascading Losses, and Economic Complexity: A Multi-layer Behavioral Network Approach

Assessing the short-term socio-economic impacts of climate-led disasters on food trade networks requires new bottom-up models and vulnerability metrics rooted in complexity theory. Indeed, such shocks could generate cascading socio-economic losses across the networks layers where emerging agents¿ responses could trigger tipping points. We contribute to address this research gap by developing a multi-layer behavioral network methodology composed of multiple spatially-explicit layers populated by heterogeneous interacting agents. Then, by introducing a new multi-layer risk measure called vulnerability rank, or VRank, we quantify the stress in the aftermath of a
shock. Our approach allows us to analyze both the supply- and the demand-side dimensions of the shock by quantifying short-term behavioral responses, the transmission channels across the layers, the conditions for reaching tipping points, and the feedback on macroeconomic indicators. By simulating a stylized two-layer supply-side production and demand-side household network model we find that, (i) socio-economic vulnerability to climate-led disasters is cyclical, (ii) the distribution of shocks depends critically on the network structure, and on the speed of supply-side and demand-side responses. Our results suggest that such a multi-layer framework could provide a comprehensive picture of how climate-led shocks cascade and how indirect losses can be measured. This is crucial to inform effective post-disaster policies aimed to build food trade network resilience to climate-led shocks, in particular in more agriculture-dependent bread-basket regions.