Improving Human Resiliency in the Face of Natural Disasters


National Science Foundation

Hurricanes routinely cause billions of dollars in damage, with the recent examples of Katrina and Harvey each exceeding $100 billion. Damage from Hurricane Katrina was so extensive that only 1/3 of the adults who evacuated New Orleans returned to their homes.Damage comes in many forms, typically from some combination of flooding, wind, and storm surge.

There are many factors that are important to families in deciding whether to evacuate in the face of an oncoming hurricane. These include household income, pets, type of home (e.g., mobile homes), and mobility of household members. It is important to understand the factors that are important to families in deciding to evacuate, and how those decisions are made and who evacuates. Furthermore, individuals/families that shelter in place (rather than evacuate) may find it difficult to find and procure basic necessities during and (immediately) after a hurricane.

Project Overview

There are two major thrusts for this on-going project. The first is to build an agent-based model to quantify the effects of different factors on the evacuation decision-making process. There are several factors that have been identified, but not quantified, and others that have not been studied. One important factor that we have studied is the fear of looting (theft and other crime) when too many people evacuate from a neighborhood. The second thrust is to identify and evaluate mechanisms for citizens to share resources during, and in the immediate aftermath of, a hurricane. As part of this second effort, we are building a web app for people to share information about available resources (what, where, how many).


We have studied the effects of looting concerns (i.e., crime) on families’ decisions to evacuate as a hurricane nears, using an agent-based model.

  1. Using survey data from Hurricane Sandy (that hit the northeastern US in 2012), we developed a bimodal model of social influence. We used that model to study the effects of looting on evacuation decision-making. Essentially, if the neighbors of a family evacuate, then that family will be more inclined to evacuate, owing to social influence. However, if too many neighbors of a family have evacuated, then that family is concerned about looting, and resists evacuating. We demonstrated through computations that concern over looting can reduce evacuation rates by up to 50%.
  2. A second work on looting included developing a more detailed model from statistical analyses of the survey data. We include family (household) factors such age, gender, race, and amount of schooling of head-of-household; type of housing; and numbers of vehicles. Neighbor influence includes the degree to which a household values the decisions of friends and neighbors, and how many of those neighbors have evacuated. A case study shows that if the police can allay citizens’ fears of looting, then evacuation rates can increase by as much as 50%. 3. Agent-based modeling can provide temporal insights about the dynamics of evacuation decision-making in a population.


Professor of Public Health Sciences, School of Medicine


Professor of Computer Science, School of Engineering and Applied Science

Network Systems Science and Advanced Computing
Kuhlman C; Marathe A; Vullikanti A; Halim N; Mozumder P . Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. 2020; :654-662
Network Systems Science and Advanced Computing
Halim N; Kuhlman C; Marathe A; Mozumder P; Vullikanti A . International Conference on Complex Networks and Their Applications. Springer. 2019; :519-531