Climate, Conflict, and Forced Migration
A large number of populations are on the move by climatic events, with a possible increase due to climate change. The terms climate migration, climate mobility, or climate refugee are recently used to highlight population movements driven by natural disasters or climate change. On the other hand, conflict and violence displace people to safer places. Despite increasing interest and urgency, we lack knowledge of migration decisions and processes that interact with climate and conflict. Existing theories are fragmented and restricted, calling for an integrated theory on the nexus between climate, conflict, and forced migration (displacement). To solve the gap, I use complex system modeling and network analysis from complex systems science.
Yearly internal displacement flows in Somalia from 2016 to 2020 (from PRMN data).
Blue arrows are flows with more than 100 people on the move, and red arrows are those with less than 100 people.
Incorporating social and natural factors in migration decisions
Published in the Journal of Artificial Societies and Social Simulation
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In this study, I developed a conceptual agent-based model (ABM) for the migration problem driven by water scarcity and social networks. I primarily focused on how multiple factors should be incorporated into the decision-making, not just looking into what factors should be considered. I compared three different factor configurations: substitutability, complementarity, and adaptability. These cases exhibited distinct patterns of population change and social mixing, offering a theoretical foundation for modeling migration decisions.
A conceptual ABM setting with migration flows and environment
Functional forms of ADD (substitutability), AND (complementarity), OR (adaptability) configurations
Emergent network patterns of Somali internal displacement: disaster vs conflict
Published in Global Environmental Change
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This study explored emergent network patterns of the internally displaced person (IDP) movement in Somalia. I particularly separated IDP networks according to the primary reasons: natural disasters (droughts & floods) and conflicts. I compare network statistics, degree distributions, triadic local structures, and clustering patterns of disaster-induced and conflict-induced IDP networks.
Due to the property of preferential attachment in scale-free networks, connections (e.g., information diffusion, clan relations) play a critical role in pulling IDPs in both disaster-induced and conflict-induced IDP networks. However, incoming IDP hubs are located in different geographical locations.
Disaster-induced and conflict-induced IDP networks share local network structures at the triad level. I consider this similarity as evidence of interactions between natural disasters and conflicts in displacement decisions.
Disasters are likely to displace people to closer districts within the regional boundaries, while conflicts are more likely to scatter people to distant areas.
"Conflict avalanches" and their clustering
Published in Royal Society Open Science
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Models and theories of armed conflict are effective when tailored to distinct conflict types, but existing classifications are often heuristic. We introduce a data-driven classification that is empirically grounded, reproducible and consistent across multiple scales. We leverage fine-grained conflict data, which we map to climate, geography, infrastructure, economics, raw demographics and demographic composition in Africa. Using an unsupervised learning model, we identify three overarching conflict types: ‘major-unrest’ at densely populated, riparian regions with well-developed infrastructure; ‘local-conflict’ in moderately populated, socio-economically diverse regions and often confined within country borders; and ‘sporadic-spillover events’ in low-population, underdeveloped areas. The three types stratify into a hierarchy of factors that highlights population, infrastructure, economics and geography, respectively, as the most discriminative indicators. Specifying conflict-type negatively affects the predictability of conflict intensity such as fatalities, conflict duration and other measures of conflict size. The competitive effect is a general consequence of weak statistical dependence. Hence, the empirical and bottom-up approach reveals how armed conflicts stratify into three archetypes, yet cautions us about the inclusion of commonly used indicators into predictive modelling.​​
Here, there are interdependent behaviors of point-level armed conflict events across regions and scales, shaping avalanches of armed conflict.
Incorporating a huge set of data, we find three data-driven clusters of armed conflict:
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Major unrest
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Local conflicts
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Sporadic/spillovers