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Color-coded threat level system (Source: Homeland Security Advisory System, dhspublic)
Source publication
Using a differential equation modeling approach, this paper explores the issue of public response to, and confidence in, anti-threat warnings. The effects of anti-threat warnings and their associated public confidence levels are modeled as a group of nonlinear differential equations. The analytical solutions of these nonlinear differential equation...
Contexts in source publication
Context 1
... protect the public and infrastructure, authorities usually issue a threat warning advisory to the public when there is a potential threat [2,3,8]. The warning advisory is issued through a five color-coded system (see Figure 1) which represents levels of risk related to a potential terror attack. Each threat level has a corresponding list of recommended actions that the public should take in order to reduce the likelihood or impact of a potential attack. ...
Context 2
... the authorities' raised threat level, as an outside force to the public, makes the system jump from the low risk state to the elevated risk state. Since the public does not know how the authorities determine the level of the risk state, if we assume such a determination is based on a number of factors unknown to the public but known by the authorities, we can use a function G ( 1 2 , , , m v v v ) to represent such a decision, where 1 2 , , , m v v v are various factors. Then we can conclude that our model system would have a sudden state jump/switch when the function G is suddenly added to the system. ...
Context 3
... terrorist attacks are significant threats to U.S. homeland security. To protect the public and infrastructure, authorities usually issue a threat warning advisory to the public when there is a potential threat [2, 3, 8]. The warning advisory is issued through a five color–coded system (see Figure 1) which represents levels of risk related to a potential terror attack. Each threat level has a corresponding list of recommended actions that the public should take in order to reduce the likelihood or impact of a potential attack. Therefore, when a warning advisory is issued, authorities hope the public will follow the advisories listed on the DHS (Department of Homeland Security) Citizen Guidance on the Homeland Security Advisory System web page and take the recommended actions [1, 4]. For example, as of March 8, 2006 the country remains at an elevated risk, code yellow, for a terrorist attack. Given this threat level, the public is recommended to take the following twelve ...
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Citations
... The warning sequence process model [49,51,53] posits that there are six phases extending from disseminating the warning to protective action. Social science theory suggests that people's response to warnings depends on the social, economic, demographic and physical/technological environment, [1,14,16,33,50] as well as message content [32,33,44,49,51,53]. People may seek additional information and confirmation through observation and direct contact [37,49,51,53]. ...
... Thus, special care should be taken to incorporate these effects in any general model for diffusion of warnings. The warning time distribution has two components: the official broadcast component [44,47,52] and the information contagion (diffusion) component [14,47,51,53]. There has been considerable research in estimating warning time distributions by modeling the warning network, however the contagion component is little understood [1,35,44,47], and needs to be better understood for protective agencies to fully estimate warning times [33,34]. ...
We consider the model of information diffusion in social networks from
\cite{Hui2010a} which incorporates trust (weighted links) between actors, and
allows actors to actively participate in the spreading process, specifically
through the ability to query friends for additional information. This model
captures how social agents transmit and act upon information more realistically
as compared to the simpler threshold and cascade models. However, it is more
difficult to analyze, in particular with respect to seeding strategies. We
present efficient, scalable algorithms for determining good seed sets --
initial nodes to inject with the information. Our general approach is to reduce
our model to a class of simpler models for which provably good sets can be
constructed. By tuning this class of simpler models, we obtain a good seed set
for the original more complex model. We call this the \emph{projected greedy
approach} because you `project' your model onto a class of simpler models where
a greedy seed set selection is near-optimal. We demonstrate the effectiveness
of our seeding strategy on synthetic graphs as well as a realistic San Diego
evacuation network constructed during the 2007 fires.
This research develops an innovative terror threat advisory system capable of visually communicating variations in the terrorism levels to policymakers or the public and forecasts future levels. Earlier attempts to create similar advisory systems by policymakers were either discontinued or lost their relevance due to a trust deficit in the system. We propose a novel approach for creating a color scheme and utilize it to develop an intuitive caterpillar diagram summarizing various stages of terrorism. It incorporates Global Terrorism Impact Scores for a nation or region using the Global Terrorism Database (GTD). Further, color transitions in the caterpillar diagram between consecutive periods mimic a Markovian process, thereby enabling us to develop the forecasting model. We successfully demonstrated the effectiveness of the proposed caterpillar diagram and forecasting model for India, Iraq, and their respective regions. The forecasting model suggests that the aggressive terrorism stage depicted by red color and transient stages of ascent (yellow) and descent (cyan) are the most probable in these nations and their regions. The proposed caterpillar diagram is an innovative visualization approach to identify terrorism patterns, from which a Markovian forecasting model is developed to aid policymakers. Our approach applies to any event-based database like GTD. Finally, the caterpillar diagram is a domain-independent framework that can visualize variations in any univariate data series, thereby assisting in system monitoring.
This paper focuses on the dissemination characteristics and the influence factors of warning in a social network. Under the guidance of complex adaptive systems and complex network theories, a dissemination model of warning by using the multi-agent software of Repast was built based on ER random network and WS small world network. A simulation was performed at different settings of social network structure and selection rules in source nodes. By analyzing the simulation results the dissemination efficiency of warning is obviously improved when the clustering coefficient is high or the source nodes are at a high level of connectivity.
In the post-9/11 world, managers and owners of large public venues face new challenges. Since stadiums are potential targets of terrorist attacks, sports venues may experience falling ticket sales as the public becomes more concerned about the threat of terrorism. This paper estimates the risk to business disruptions by examining how baseball fans respond to increased terror-alert levels by the U.S. Homeland Security Advisory System. Using game-day attendance for each Major League Baseball team for the period 2002-03, the results indicate that during the early days of the nation's firstever increase in the terror-alert level (i.e., raised from yellow to orange) attendance decreased by as much as 12%. A back-of-the-envelope calculation suggests that a team may have lost more than a half million dollars in attendance revenue. However, as found in the literature, subsequent increases in terror-alert levels appear to dull the public, since recent alerts no longer affect attendance.
Quick and highly efficient warning is critical in any emergency evacuation. From the issuing of warning until action taken by people in response, the entire course is a very complex process which is restrained by social, economic, population and other factors. To study the diffusion characteristics of warning in the social network environment as well as the relevant influencing factors, under the guidance of complex system theory and multi-agent modeling method, this paper analyzes the six stages in the diffusion process of warning and builds a diffusion model of warning on the multi-agent software platform Repast based on WS small world network model. Through the study of the simulation results, we find that the diffusion of warning is similar to the diffusion of other information, which showed a similar S-curve.
Our understanding of the impacts of social media on individuals who receive warnings of extreme events is limited. There is to date no uniform approach to integrating social media as part of emergency management strategies. This research addresses the question of the role of social media in the effectiveness of the warning response process in the context of a naturally occurring experiment. The results of the experiment contribute to our understanding of how social media complements as well as facilitates the warning response process.
