Affiliation:
1. University of Nebraska-Lincoln
2. The Citadel, Charleston, SC
Abstract
Prior research suggests that the timing and location of social unrest may be influenced by similar unrest activities in another nearby region, potentially causing a spread of unrest activities across space and time. In this paper, we model the spread of social unrest across time and space using a novel approach, grounded in
agent-based modeling (ABM)
. In it, regions (geographic polygons) are represented as agents that transition from one state to another based on changes in their environment. Our approach involves (1) creating a vector for each region/agent based on
socio-demographic, infrastructural, economic, geographic, and environmental (SIEGE)
factors, (2) formulating a neighborhood distance function to identify an agent's neighbors based on geospatial distance and SIEGE proximity, (3) designing transition probability equations based on two distinct compartmental models—i.e., the
Susceptible-Infected-Recovered (SIR)
and the
Susceptible-Infected-Susceptible (SIS)
models, and (4) building a ground truth for evaluating the simulations. We use ABM to determine the individualized probabilities of each region/agent to transition from one state to another. The models are tested using the districts of three states in India as agents at a monthly scale for 2016-2019. For ground truth of unrest events, we use the
Armed Conflict Location and Event Data (ACLED)
dataset. Our findings include that (1) the transition probability equations are viable, (2) the agent-based modeling of the spread of social unrest is feasible while treating regions as agents (Brier's score < 0.25 for two out of three regions), and (3) the SIS model performs comparatively better than the SIR model.
Publisher
Association for Computing Machinery (ACM)
Subject
Discrete Mathematics and Combinatorics,Geometry and Topology,Computer Science Applications,Modeling and Simulation,Information Systems,Signal Processing