## 5 Discussion
For example, our calculations show that for a specific area, there can be defined a comparison between the existing system (Amber Alert) and the alternative through the use of a mobile app, in terms of probability of a sighting and a positive identification of the missing child, depending on the number of people moving and having received the mobile notification with the details of the missing child. Of course, this system, as any other simulation model, is not above limitations. First, the simulation model is highly dependent on the initial conditions and the decisions taken for its many parameters. A change in one or more of these parameters may give different results for the critical mass of users. Therefore, especially for data that may be noisy to begin with, careful selection of the appropriate variables needs to take place. Where sensitive factors may heavily impact the results of the simulation, the related variables need to be based on more or more carefully selected data. For example, although deviations in the average walking/driving speed in an area does not seem to affect the results a lot, the amount of time the missing child rests before moving to another location, or the probability of a positive identification when a chance encounter with an app user takes place seem to have greater impact. A limitation we have identified is that the coverage of an area is dependent on both the number of users as well as their distribution. Our simulation framework examines only the former, while it considers the users uniformly distributed. In that view, simulation of large areas, such as a city, would be less accurate, and the need arises to split that area into smaller ones. The model is parameterised on a generic level, and the indications on the users' number apply uniformly to the whole area without considering neighbourhood-level specificities. Splitting the large area into many smaller ones partially solves the problem but significantly increase the computational complexity and presents a challenge as to the criteria and configuration for this split. On the other side, extrapolating the results of a small region to larger areas, such as a city, would also involve risks. The micro-geography of a region, namely, the small scale of detail on its geography and street structure, affects how people are moving within it. What is more, the actual micro-geography of any geographic area, such as a city, is still overall unexplored [74] (p. 77), which may also impact the results' accuracy. A proposed method to circumvent these issues for larger areas is instead of applying a uniform two-dimensional distribution for the starting locations of the agents in each simulation, namely, the people's homes, to apply a nonlinear distribution of probabilities that the starting location (the home) of a user is in each point in the map. This probability distribution can be derived from mobile user data with more granularity, point population densities per municipality, averaging the points between them, or other similar methods. This would mean that the geographic user density in a set of simulations will match that of the population. Additionally, some challenges remain for ABM in general, which also apply here, regarding the difficulty to validate the resulting model. These were the challenges that led us to use simulation in the first place. However, as mentioned before, collecting data from the actual implementation of ChildRescue is a potential source of data to validate the system. The actual adoption of the ChildRescue mobile application by missing children organisations and the public in Greece and Belgium is ongoing. The platform has already been used in more than 85 real-life cases (March 2021). Therefore, our next steps involve validating the simulation results with the actual ones leveraging the growing adoption rates in these two countries. Furthermore, we had to make several assumptions and simplifications about the citizens and the missing child to build the model.