The Perception of Terrorist Events to Improve Risk Communication: Understanding the Impact of Near-Miss Events and Terrorist Risk Factors

Principal Investigator: Robin Dillon-Merrill


The primary focus of the research at Georgetown University has been to understand how people perceive terrorist events in an attempt to improve future risk communication efforts. One specific focus is on the perception of near-miss terrorism experiences, and the lasting impact of these events over time. We demonstrate that when near-misses are interpreted as disasters that did not occur and thus provide the perception that the system is resilient to the hazard, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions. On the other hand, if near-misses can be recognized and interpreted as disasters that almost happened and thus provide the perception that the system is vulnerable to the hazard, this will counter the basic “near-miss” effect and encourage mitigation (at least initially). In this research, we use these distinctions between resilient and vulnerable near-misses to examine how people come to define an event as either a resilient or vulnerable near-miss, as well as how this interpretation influences perceptions of risk and mitigation behavior. The objectives are to (a) further develop the system dynamics and agent-based models to examine the spread of the effects of risk-communication messaging throughout a population to those exposed to the message and also to those who learn of the message content by word of mouth; (b) test the efficacy of inoculation based risk-communication messages as a means to increasing public resilience to different hazards; (c) using both the results of the system dynamics and agent-based modeling in conjunction with CGE modeling estimate the economic value of a risk-communication program; (d) further develop the agent-based model to include agents whose decisions dynamically take into account stochastic events in their environment (e.g., seek treatment after learning they have been exposed to anthrax); (e) imbue agents with attribute weights that reflect the distribution found in the population (e.g., how important is the type of mishap versus proximity to the mishap in the decision to seek treatment, evacuate and so forth?) and (f) begin to develop signature narratives for event types such as earthquakes, technological accidents, mass shootings like a Newtown and different types of terrorism such as lone-wolf attacks (e.g. Boston Marathon) that guide the unfolding of events that agents can respond to during the simulation (e.g., trajectory of event intensity, likely media coverage).