Modeling and Simulating Command and Control for Terrorist Organization
Over time, people change with whom they interact and where they are. For instance, terrorists attempt different tasks, move to new locations, and interact with different groups. Understanding how changes in social and geospatial relations interact is crit
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Modeling and Simulating Command and Control for Terrorist Organization
Keywords Command and control Organizational goal Geospace Global terrorist network Transactive memory Dynet OrgaHead Construct Metanetwork AutoMap Multiagent simulation Homophily Knowledge diffusion Task accuracy Sensitivity analysis Gini coefficient Meta model
2.1 Introduction As the command and control is an effort to achieve the organizational goal, the command and control structure should be designed to optimize crucial factors affecting the outcome. The interaction structure between the commanders and the units is one of such factors, and their geospatial locations are another critical factor. This is particularly significant when we consider a command and control structure that spans social groups as well as covers large geospace, i.e., a global terrorist network. We analyze the command and control structure of a global terrorist network1 that we estimate from network text analyses. Fundamentally, where social agents are influences who the agents know, and vice versa. As the agents move to new cities or countries, their contacts change. For instance, when a company relocates employees, they develop new working relations with others while they perform assigned tasks. In theory, relocation should improve performance. However, we also know that performance is dependent on knowing who to ask about what, i.e., transactive memory (Wegner 1986). Moving disrupts this memory and also the social relations by which information flows. Thus, we ask whether performance can improve when the geospatial and social distribution change simultaneously.
1 This case study is introduced by Moon and Carley (2007). This chapter expands the initial publication with additional background, dataset description, and virtual experiment results. Also, at the end of this chapter, we discuss how to interpret the result in the context of the command and control.
I.-C. Moon et al., Modeling and Simulating Command and Control, SpringerBriefs in Computer Science, DOI: 10.1007/978-1-4471-5037-4_2, Il-Chul Moon 2013
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2 Modeling and Simulating Command
These social and spatial relations evolve over time, so does the command and control structure that the relations imply. Estimating the evolutions is an important issue for management, command and control structure, and intelligence analysis research. By knowing the future social and spatial distributions of agents, an analyst can identify who will be an emergent leader, where will be a hot spot, and what will be the vulnerability of the organization. Historically, the estimation has heavily depended on qualitative analysis (Arquilla and Ronfeldt 2001) by subject matter experts. A few researchers have approached this issue using multi-agent models and simulation from two perspectives, the impact of change in the social network (Carley et al. 2001; Snijder et al. forthcoming) and the impact of geospatial change (Epstein et al. 2001; Bergkvist et al. 2004). Their models c
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