29 June 2021
Virtual / Geneva, Switzerland / New York, USA
The Global Humanitarian Overview 2021 estimates that 235 million people are in need of humanitarian assistance, with 160 million targeted for assistance in over 30 countries. This annual estimate is based on needs assessments that take place in humanitarian crises during the year, an often-challenging process that takes account of multiple sectors. Although Humanitarian Needs Overviews increasingly include scenarios and projections for potential shocks and hazards, the coexistence, correlation, and causality of needs is difficult to capture in complex humanitarian crises.
Complex systems modelling is the application of diverse mathematical, statistical and computational techniques to generate insight into how some of the most complicated physical and natural systems in the world function. In the humanitarian system, this could mean modeling the relationship between drought, food insecurity and displacement, and what that means for overall needs.
In this webinar from the OCHA Centre for Humanitarian Data, colleagues from academia, NGOs and the United Nations will share experiences and provide concrete examples of how complex systems modeling can inform policy-making and response planning in different contexts such as disaster risk reduction or drought-induced displacement.
Last edited: 25 June 2021
Research Scholar Exploratory Modeling of Human-natural Systems Research Group - Advancing Systems Analysis Program
Dunz, N., Tanaka, H., Shiiba, N., Mochizuki, J. , & Naqvi, A. (2021). Building Back Better in Small Island Developing States in the Pacific: Initial Insights from the BinD Model of Disaster Risk Management Policy Options in Fiji. ADBI Working Paper 1290. Asian Development Bank Institute
Naqvi, A. (2021). Decoupling trends of emissions across EU regions and the role of environmental policies. Journal of Cleaner Production 323, e129130. 10.1016/j.jclepro.2021.129130.
Naqvi, A. & Monasterolo, I. (2021). Assessing the cascading impacts of natural disasters in a multi-layer behavioral network framework. Scientific Reports 11, e20146. 10.1038/s41598-021-99343-4.
Dunz, N., Naqvi, A. , & Monasterolo, I. (2021). Climate Sentiments, Transition Risk, and Financial Stability in a Stock-Flow Consistent Model. Journal of Financial Stability, e100872. 10.1016/j.jfs.2021.100872. (In Press)
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