## 3 The ChildRescue Simulation Framework
### Methodology
They may also go directly to a 'leisure activity' if it is a weekend (Figure 1b). When at 'work' or the location of the 'leisure activity', other functions define the terms for their transition to a new state. The missing child, on the other hand, moves with a user-adjustable speed depending on age. The location, date, and time that the child was last seen are also parameters of the system initiated by the simulator. The simulator can also enter one or more Points of Interest (POIs) for the child, such as playgrounds, schools, and friends' houses. The missing child then moves either to one of these POIs or a randomly selected location on the map, using a stochastic model, the values of which can be modified by the simulator for each experiment, based on the expected validity of the information they have for the child. It is also assumed that the child will rest for a random period inserted by the simulator, based on the child's age and the data, in each'shelter' destination, before going to a new destination, as depicted in Figure 2b. These parameters have been co-decided with three missing children response organisations--the Smile of the Child in Greece, Child Focus in Belgium, and the Hellenic Red Cross--along with MCE, being the umbrella organisation of all EU missing children response organisations [86]. These organisations actively participated in validating and testing the developed mobile application and are now its first users. The missing child's parameters in the simulation define the child's disappearance's initial conditions and expected behaviour based on these organisations' and operators' experiences and expertise. The interaction among the citizens and the missing child is represented by another function, according to which at each simulation cycle, the people agents are looking for whether the missing child is somewhere near them. Even if a citizen comes in easy reach of the child in the simulation, the child is not necessarily considered as found. The citizens may be distracted or may fail to recognise the child from the public pictures distributed through the mobile app, as the academic literature suggests [87; 88]. However, we consider this value of destruction less intense than the current situation since the child's picture will be just a click away on their mobile rather than the once-seen picture on an Amber alert. The transportation means used by the citizen and the missing child at the meeting time also play a crucial role. They outline the velocity of the citizen and the missing child at their encountering and, consequently, its duration. A probabilistic model of discovery for the missing child has been developed to implement this behaviour in the system, according to which there is a different adjustable probability for the citizens to identify the missing child based on whether they are driving, walking, or resting. Whether the agents are inside or outside a building at the meeting time is also considered in our probabilistic discovery model. In total, 29 parameters are used to calibrate the maps, the missing child's movement, mobility, and probabilities of identifying the child according to the circumstances of the encounter. Apart from the default simulation execution mode, where the simulator supervises the experiment by watching the agents move across the map, a batch experiments' mode has also been implemented. Batch experiments are consecutive experiments performed for multiple iterations until a user-defined condition is met. These experiments aim to test one specific parameter and how its value affects the results. Therefore, in each iteration, only this parameter's value changes. In our case, we aim to explore the impact of the application users' number on the time it takes to find the missing child. In this context, two types of batch experiments have been developed. In both types, only the number of the people agents changes.