Version 1
: Received: 11 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (11:39:18 CEST)
How to cite:
Drury, K.; Smith, J. Q. Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments. Preprints2024, 2024091004. https://doi.org/10.20944/preprints202409.1004.v1
Drury, K.; Smith, J. Q. Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments. Preprints 2024, 2024091004. https://doi.org/10.20944/preprints202409.1004.v1
Drury, K.; Smith, J. Q. Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments. Preprints2024, 2024091004. https://doi.org/10.20944/preprints202409.1004.v1
APA Style
Drury, K., & Smith, J. Q. (2024). Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments. Preprints. https://doi.org/10.20944/preprints202409.1004.v1
Chicago/Turabian Style
Drury, K. and Jim Q. Smith. 2024 "Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments" Preprints. https://doi.org/10.20944/preprints202409.1004.v1
Abstract
Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.
Keywords
Bayesian networks; dynamic Bayesian networks; decision support systems; expert judgement; elicitation; model libraries; missing data; crime intervention; causality
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
