## 3 The ChildRescue Simulation Framework
### Forming the Baseline  
To form the baseline of our experiment, the current situation for the missing children issue was captured based on the information provided by the participating organisations mentioned before. Fourteen bilateral meetings and interviews took place, and past resolved cases were collected to set up the experiments' baseline on both their expertise and the data. A dataset of 121 past cases was provided, including a unique case ID for each record and other information about the child and the disappearance conditions, such as gender, age, location last seen, date and time of disappearance, and date and time  
Figure 2: Behaviour and states of: (**a**) the people agents and (**b**) the missing child. found. The dataset on which this analysis was based may seem small. However, it well represents the cases that the organisations that provided them have handled in the last 10 years. For the categorisation of the cases, all three organisations have adopted MCE's categorisation system, according to which there are five types of missing children cases, (a) runaways, implying voluntary leave; (b) third-person abuctions; (c) parental abuctions; (d) missing unaccompanied migrant minors; and (e) lost, injured, or otherwise missing children [89]. Each type demonstrates different characteristics studied a lot by the organisations' social workers and, therefore, different prospects for their successful resolution, as depicted in Table 1 providing summary statistics for the resolved cases of the dataset per missing type. The past cases analysis also showed a statistically significant relation ( _p_-value = 3.796 \(\times\) 10\({}^{-1}\)) among the missing type and the child's age group when excluding the 'lost, injured, or otherwise missing' category that incorporates all cases with unknown circumstances of disappearance. The Chi-square test of independence was conducted, ensuring that no more than 20% of the cells have an expected frequency lower than five. The Bonferroni-adjusted method was then used as a post hoc test to identify the age groups and missing types responsible for creating a significant relationship. A dependence among runaways and adolescence (age 10 onwards), on the one hand, and parental abductions and early childhood, on the other hand, was identified. Through statistical significance tests, a correlation among the missing type and gender also emerged, indicating that more females than males tend to run away and that there is a higher proportion of male unaccompanied migrant minors. In rough numbers, most of the dataset's disappearances are runaways (52%), which conforms with MCE's annual reports [90]. It is followed by missing unaccompanied migrant minors (31%), a case type not well represented in the official reports. Of the 23 surveyed organisations in 2019, only 47.6% reported that they work on missing children in migration, and only five organisations could provide more detailed information on the time it took before the missing minors were recovered [90] (p. 6). Cases of children in migration take the longest to be solved, if solved at all, as also indicated by Table 1. Thus, it is not the most representative case to work on. It can be seen in Table 1 that there is significant variation in the data, which renders the determination of a baseline value for the resolution of cases challenging. Each child is unique, and so is each missing child case. Despite their differences, though, there are also commonalities, especially for cases of the same type. Therefore, for the simulation, we have assumed the case of a missing adolescent running away from home. The 'runaway' missing type has been selected as it is the most representative category and the most dependent on the missing child's behaviour rather than external factors (e.g., abductor).