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| Titel | LITERATURE REVIEW OF EXISTING TERRORIST BEHAVIOR MODELING Final Report to the Defense Threat Reduction Agency |
| Herausgeber | Center for Nonproliferation Studies |
| Beteiligte | Amy Sands, Jason Pate, Gary Ackerman, Anjali Bhattacharjee, Matthew Klag, Jennifer Mitchell |
| Datum | 14. August 2002 |
| URL | http://cns.miis.edu/reports/pdfs/terror_lit.pdf |
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| Fußnoten | no |
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| Valdis Krebs [FN 12] worked in analyzing organizations, especially adaptive organizations. He has applied real-world data and model “social network processes”. He has done some work on analyzing the terrorist network surrounding the 9/11 attacks (Krebs, V.; 2001, 2002). He states that one can also apply his analysis to counterterrorist organization and believes it takes a network to fight a network; he therefore prescribes the use of small anti-terror teams.
In his approach, Krebs (2002) takes a snapshot of a network. After many such snapshots, he can see how networks evolve. He argues that one can see patterns at the planning stage of terrorist attacks - a terrorist planning team looks like any other planning team. So, once they get into active planning mode, terrorists’ project map looks like anyone else’s. One can use this knowledge to analyze groups and see where they are in the operational process. Krebs (2002) also emphasizes that terrorists, like other organizations, do have leaders, so one does not necessarily need all the data – we can obtain a lot of information without it. There has been a great deal of work in link analysis in law enforcement. Link analysis focuses more on objects and people, whereas Krebs’ software concentrates on people and uses social network metrics. Roger Smith (Smith, R., 2001) presented a definition of social cohesion based on network connectivity that leads to an operationalization of social embeddedness. He defined cohesiveness as the minimum number of actors who, if removed from a group, would disconnect the group. Borgatti Stephen P. (2003) discussed how to identify sets of structurally key players, particularly in the context of attacking [terrorist networks.] [FN 12] http://www.orgnet.com/VKbio.html |
- Mr. Krebs works in analyzing organizations, especially adaptive organizations. He would like to apply what
he does to real-world data and model ‘social network processes’. - He has done some work on analyzing the terrorist network surrounding the 9/11 attacks. He states that one can also apply his analysis to counterterrorist organization and believes it takes a network to fight a network; he therefore prescribes the use of small anti-terror teams. - In his approach, Mr. Krebs takes a snapshot of a network. After many such snapshots, he can see how networks evolve. He argues that one can see patterns at the planning stage of terrorist attacks - a terrorist planning team looks like any other planning team. So, once they get into active planning mode, terrorists’ project map looks like anyone else’s. One can use this knowledge to analyze groups and see where they are in the operational process. Mr. Krebs also emphasizes that terrorists, like other organizations, do have leaders, so one does not necessarily need all the data – we can obtain a lot of information without it. - There has been a lot of work in link analysis in law enforcement. Link analysis focuses more on objects and people, whereas Mr. Krebs’ software concentrates on people and uses social network metrics. [page 95] We present a definition of social cohesion based on network connectivity that leads to an operationalization of social embeddedness. We define cohesiveness as the minimum number of actors who, if removed from a group, would disconnect the group. [page 172] This paper discusses how to identify sets of structurally key players, particularly in the context of attacking terrorist networks. |
The given internet link does (and did not) give the same information let alone in the same formulations as the source. |
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| [Borgatti Stephen P. (2003) discussed how to identify sets of structurally key players, particularly in the context of attacking] terrorist networks. Three specific goals are discussed: (a) identifying nodes whose deletion would maximally fragment the network, (b) identifying nodes that, based on structural position alone, are potentially “in the know”, and (c) identifying nodes that are in a position to influence others. Measures of success, in the form of fragmentation and reach indices, are developed and used as cost functions in a combinatorial optimization algorithm. The algorithm is compared against naïve approaches based on choosing the k most central players, as well as against approaches based on group centrality. | This paper discusses how to identify sets of structurally key players, particularly in the context of attacking terrorist networks. Three specific goals are discussed: (a) identifying nodes whose deletion would maximally fragment the network, (b) identifying nodes that, based on structural position alone, are potentially “in the know”, and (c) identifying nodes that are in a position to influence others. Measures of success, in the form of fragmentation and reach indices, are developed and used as cost functions in a combinatorial optimization algorithm. The algorithm is compared against naïve approaches based on choosing the k most central players, as well as against approaches based on group centrality. |
The source is not given. The copied text starts on the previous page. |
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Professor Carley and her colleagues are working on a number of projects related to counterterrorism. All their models contain AI, complexity approaches, and are multi-agent.
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Professor Carley described six ongoing projects related to counterterrorism being conducted by her research group. All their models contain AI, complexity approaches, and are multi-agent.
[page 105] We describe a simulation system called BIOWAR which uses cognitively realistic agents embedded in social, knowledge and work networks to describe how people interacting in these networks acquire disease, manifest symptoms, seek information and treatment, and recover from illness. Using a model of diseases and symptoms, agents who come in contact with infectious agents through their social and work networks become ill. These illnesses alter their behavior, changing both the propagation of the disease, and the manifestation of the disease on the population. Presently, we have completed a number of simulations which examine the effect of contagious and non-contagious illnesses in high-alert (agents have knowledge of a potential disease outbreak) or low alert states. Agents who believe they may be ill and have knowledge of a potential outbreak are more likely to seek care than those who do not. |
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[In this] system authors compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. They hoped that this simulation framework will allow them to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics. This is a city scale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data -hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.
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[page 105]
We have compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. [...]. It is hoped that this simulation framework will allow us to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics. [page 18] this is a cityscale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data - hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model. [...] DYNET – Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination |
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| THREATFINDER – this tool originated in the corporate
realm. Certain individuals within an organization are more likely than others to pose threats. The research involves looking for threat indicators for who could launch an attack i.e. using a network/organizational perspective, Threatfinder identifies threats within an organization. Tsvetovat Maksim and Kathleen Carley (2002) proposed a methodology for realistically simulating terrorist networks in order to develop network metrics to test strategies of destabilizing them, provided a model of antiterrorist policy. They have not discussed behavior or real group interaction and the model is limited in scope and depth. It may be used as a starting point to focus terrorist organization and network Michael J. North and his colleagues (North, J. M; Nicholson, T. C; and Jerry R. V., 2006) worked on a model of terrorist networks— looking at relationships between different terrorist structures. Their models show whether or not particular terrorist group fits in a given structure. The authors modeled terrorists as genetic algorithms. The models also look at counter forces. Social factors, such as propagation of dangerous ideas, are built into the model. The model uses REPAST tool (developed by University of Chicago) which, as opposed to SWARM (which they believe is getting old). |
[page 18]
THREATFINDER – this tool originated in the corporate realm. Certain individuals within an organization are more likely than others to pose threats. The research involves looking for threat indicators for who could launch an attack i.e. using a network/organizational perspective, Threatfinder identifies threats within an organization. [page 20] The paper proposes a methodology for realistically simulating terrorist networks in order to develop network metrics to test strategies of destabilizing them; provides a model of anti-terrorist policy; doesn't provide behavior or real group interaction and is limited in scope and depth; may be used as a starting point; focus: terrorist organization and network [page 14] - He is working on a model of terrorist networks – looking at relationships between different terrorist structures. - His models let people test whether or not a particular terrorist group fits a given structure. - Terrorists are modeled as genetic algorithms. - The model also looks at counterforces. - Social factors, such as the propagation of dangerous ideas, are built into the model. - The model uses REPAST (developed by the University of Chicago) which is written in Java, as opposed to SWARM which is written in C (and he believes is getting old). |
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