6 138 CHAPTER 6 network are also key. From this follows the proposition that increased risk of sexual reoffending is characterized by a network of more and stronger interconnected dynamic risk factors having a higher degree of activity (i.e., being more strongly presence, for example expressed in terms of a higher score on a dynamic risk assessment instrument). Future research could test the following two hypotheses. First, the predictive accuracy of algorithms to estimate the risk of sexual reoffending is expected to be larger when the density and connectivity of the network will be taken into account above and beyond the number and strength of dynamic risk factors. Second, both the density and connectivity of the network of dynamic risk factors is expected to be predictive of reoffending risk in participants matched on number and strength of dynamic risk factors. 6.3.1.2 Network stability and critical transitions Recent research involving repeated assessments of dynamic risk factors using the ACUTE-2007 (Babchishin & Hanson, 2020) suggests that although the probability of reoffending may change over time, substantial variability exists in the degree of change. That is, in any given follow-up period the likelihood of reoffending has been found to change for some individuals, while others show a stable risk (Babchishin & Hanson, 2020). From the NBM-RSR, both the relative stability of and changes in risk of sexual reoffending can be understood from self-sustaining networks of dynamic risk factors and their critical transitions (Hayes & Andrews, 2020; Kossakowski, 2020). Future research could examine the proposition of the existence of the relative stability of the self-sustaining network of dynamic risk factors and critical transitions to changed levels of risk. Assuming the existence of critical transitions between two different states in a self-sustaining network, we hypothesize that within-system changes in dynamics indicative of a transition from one state to another – called early warning signals – will be found in the network of dynamic risk factors of adult males convicted for sexual offenses (Kossakowski, 2020; Scheffer et al., 2012). 6.3.1.3 Dynamic risk factors’ relative influence on risk Another proposition can be derived from the NBM-RSR on the relative influence on risk of sexual reoffending of specific dynamic risk factors. According to our model, dynamic risk factors’ influence within the network, and therefore their influence on risk, increases due to: (a) an upsurged number of relatively strong interrelations with other dynamic risk factors (i.e., having a higher centrality), and (b) by forming a connection, or bridge, between two or more communities of dynamic risk factors (Castro et al., 2019; McNally, 2016; Opsahl et al., 2010; van den Berg et al., 2022). Simulation studies conducted with 51 cross-sectional psychopathological networks have found moderate evidence to sustain the hypothesis that central and bridge symptoms indeed have a relatively stronger influence (Castro et al., 2019). Future simulation studies could examine to what extent this hypothesis applies to networks of dynamic risk factors relevant to sexual
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