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Abstract Criminal networks are frequently at risk of disruption through arrest and interorganizational violence. Difficulties in designing empirical studies of criminal network recovery, however, have problematized research into network responses to disruption. In this study, we evaluate criminal network resilience by examining network recovery from disruption in an array of different criminal networks and across different disruption strategies. We use an agent‐based model to evaluate how criminal networks recover from disruption. Our results reveal the vulnerabilities and time to recovery of numerous criminal organizations, and through them, we identify which disruption strategies are most effective at damaging various criminal networks.
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Kim, I., Frank, K., Wolf, J.R., & Pentland, B.T. (2021) “Predicting Process Structure after a Disruption: An Example from the Clinical Documentation Process”
Using data from the audit trail of an electronic medical record system, we examine the effects of a disruption on the clinical documentation process. We use process mining to construct a network that describes the process and then we use a latent factor selection model to analyze changes to that network. Rather than attempting to discover a particular process model, our goal is to identify theory-based factors that explain change and stability in the overall pattern of actions. We conduct the analysis at two levels of granularity and we compare time periods with and without disruption. The paper contributes to current research on routine dynamics as network dy-namics by demonstrating the use of network science to predict the structure of an organizational routine.
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- Award ID(s):
- 1734237
- PAR ID:
- 10302054
- Date Published:
- Journal Name:
- BPM Routine Dynamics Workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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