Dr. Leen-Kiat Soh
Dr. Ashok Samal
Date of this Version
Social unrest activities are the tools for people to show dissatisfaction, and often people are motivated by similar unrest activities in another region. This causes a spread of unrest activities across space and time. In this thesis, we model the spread of social unrest across time and space. The underlying novel methodology is to model the regions as agents that transition from one state to another based on changes in their environment. The methodology involves (1) creating a region vector for each agent based on socio-demographic, cultural, economic, infrastructural, geographic, and environmental (SCEIGE) factors, (2) formulating neighborhood distance function to identify the neighbors of the agents based on geospatial distance and SCEIGE proximity, (3) designing transition probability equations based on infectious disease spread models, and (4) building groundtruth for evaluating the simulations. We implement two different social unrest spread models based on two infectious disease models, SIR and SIS. Here we use the concept of contact networks and find the individualized probabilities of each agent to transition from one state to another, which is often used in the infectious disease spread model to establish contact leading to disease in the individual. In our case, we use the contact networks to establish contact leading to social unrest in an agent. The models are tested on India, particularly in the three states, Tamil Nadu, Andhra Pradesh, and Himachal Pradesh, for 2016-2020 on a monthly scale. For the SCEIGE factors, we use labor wages, road density, gross domestic product, number of hospitals, and standard precipitation index sourced from national and international institutes and agencies. For groundtruth, we use the ACLED dataset on political violence and protest. Our findings include (1) the transition probability equations are viable, (2) the agent-based modeling of the spread of social unrest is feasible while treating each region as an agent, which is the novelty of our approach, and (3) the SIS model performs comparatively better than the SIR model.
Advisors: Leen-Kiat Soh and Ashok Samal