The use of Bayesian Networks and simulation methods to identify the variables impacting the value of evidence...

This paper presents a methodology allowing identification of the variables associated with transfer, persistence or recovery of DNA traces that have the most significant impact on the result of an evaluation measured through a likelihood ratio (LR). It builds on a case scenario involving trace DNA recovered from knife handles where the prosecution alleges that the person of interest (POI) stabbed a victim whereas the defence claims that the POI has nothing to do with the incident and the victim was stabbed by an alternative offender (AO). The defence proposition will also be refined to account for the possibility of secondary transfer. The variables having a significant impact on the LR are identified taking advantage of a graphical probabilistic environment (using Bayesian Networks, BN), coupled with simulation techniques. The paper presents (a) a BN, based on previous work Taylor et al. [5]; (b) its parametrization based on the current literature that represents the current state of knowledge used to inform the conditional probability tables of the BN and; (c) the implementation of the simulation methods. Results show that, regardless of the DNA outcome obtained, the most impacting variable is the "DNA match probability" when the defence alleged that the POI has nothing to do with the incident. It means that, given the current state of knowledge, such cases can easily be interpreted considering activity level propositions as they would not require any further data acquisition. When secondary transfer is alleged under the defence's perspective, the LRs are generally much lower than for the previous case. The DNA match probability has less impact and variables associated with the donor will take the lead on the ranges observed on the LRs. Overall, once extraction and sampling efficiency have been set, the remaining variables that have an impact on the value of the evidence are the DNA quantity on hands and the background. With the most impacting variables so identified, it becomes manageable to direct further data acquisition if so required. Generally, the background that could be present on the knife handle, the environmental conditions are not critical due to their limited impact on the LR value. We note, however, that this identification of the significant variables depends on the obtained DNA results and this selection may be refined on a case by case basis. To allow one to explore all possibilities a dedicated Shiny application has been designed (https://lydie-samie.shinyapps.io/DNA_Activity/).