CSH Talk by Sebastian Poledna: “Linking natural disasters with socio-economic systemic risk through a data-driven agent-based model”

Linking natural disasters with socio-economic systemic risk through a data-driven agent-based model

Sebastian Poledna, CSH Associate Faulty

The lecture will take place at the Complexity Science Hub Vienna in Room 201.

Abstract:

This paper describes a novel approach for estimating the indirect economic losses due to natural disasters with an application to flood events in Austria. The approach combines a probabilistic physical damage catastrophe model with a macroeconomic agent-based model (ABM). The ABM methodology advances state-of-the-art approaches by exploiting large data sets (big data) with close to a million agents (households, non-financial and financial firms and a general government) calibrated with data from national accounts, input-output tables, government statistics, census data and business information. The probabilistic flood model is equally innovative by introducing a copula methodology that provides an assessment of flood losses by taking account of spatial dependencies in the flood hazard.

The ABM includes an input-output model with 64 industries where all goods and services are produced endogenously, and the probabilistic copula approach provides a nation-scale estimate of direct flood losses over the full risk spectrum based on basin-scale loss distributions and exposure with Corine land-cover mapping. The direct loss estimates are used to build a damage scenario generator that provides the input for the ABM, which, in turn, assesses the indirect economic losses due to the event.

The analysis shows that there can be severe indirect economic losses in Austria due to large-scale natural disasters, or systemic events, and shows the impact chains leading to the systemic losses. One of the main findings is that the distinct types of disaster events exhibit qualitatively different economic behavior: while more moderate scenarios induce positive indirect economic effects in the medium term, the severe, or systemic, events result predominantly in a negative economic response throughout the simulation period. Most importantly, the analysis disaggragates the gains and losses occurring to different sectors. This detailed information can be useful for assessing risk management options at various scales.