There are limitations to traditional methods of capturing the dynamics of violent interactions. These limitations are due to outcome driven approaches, data sampling issues, and inadequate means to capture, express, and explore the complexity of behavioral processes. To address these challenges, it is proposed that “violent offending” be re-framed as an emergent feature of a complex adaptive social system. This dissertation abstracts and computationally implements a theoretical framework that forms the basis of a complex social simulation of the violent offending process. The primary outcome of this effort is a viable synthetic offender that emerges from simulated interactions between potential offenders (subjects) and potential victims (targets) within an environment. The results of calibrating this model to a real-world murder series are discussed, as well as, the comparison metrics used to assess goodness-of-fit of simulated and real-world event-sites. A synthetic offender promises valuable insights into individual offending trajectories, offender tactical processes, and the emergence of geospatial and temporal behaviors. Furthermore, this approach is capable of reproducing the violent offending process with sufficient detail to contribute new scientific understanding and insights to criminology and the social sciences.
|Commitee:||Crooks, Andrew, Jarvis, John, Kennedy, William|
|School:||George Mason University|
|Department:||Computational Social Sciences|
|School Location:||United States -- Virginia|
|Source:||DAI-A 77/10(E), Dissertation Abstracts International|
|Subjects:||Behavioral Sciences, Criminology, Computer science|
|Keywords:||Agent-based modeling, Computational criminology, Serial murder, Synthetic offender, Violent behavior, Violent offender|
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