## 5 Discussion  
In this paper, we have built a stochastic, bottom-up simulation framework for citizen sensing crowdsourcing applications, together with a proof of concept on the investigation of missing children. It shows that the effectiveness of such applications is a function of the number of users they have, which in turn can be approximated and studied. This can be completed if the simulation model is scalable, allowing for heterogeneity in individual characteristics (e.g., agendas, working hours, travel speed, missing child's movement). This framework relies on geographical and aggregated demographic data. Since the simulations aim to approximate, rather than predict, cases of investigations for missing children, demographic aggregates are enough for the required granularity of the test runs. As the real cases are random events in a fluid, but probabilistically predictable environment, so are the simulated ones. This leads us to conclude that the simulations do not need to replicate the real world, but rather provide statistical results that are comparable with it. Further refinement of the model is easily achievable, with the use of more data on human mobility in an area, and of course, allow the simulations to run for other locations, but for the scenario investigated in this paper, more data will not significantly alter the statistical results of the simulations. In the end, the goal of this framework is to provide a decision support tool for organisations that need to predict the effectiveness of the collective awareness mobile tools they provide to form a strategy for reaching the market penetration they need to have. Agent-based modelling and simulation have been primarily used in many applications to analyse the relation of different variables and their mutual influences in a spatiotemporal context. They intended to unfold valuable insights for the spatial dynamics developed when simulating a complex system such as that of the natural world by simulating people's bottom-up behaviour [99; 100]. Our model is built on the knowledge the academic literature has acquired to develop a new urban simulation of human mobility able to answer complicated questions, opening up capabilities for other applications. In particular, considering the use of the adapted simulation model on any similar citizen sensing mobile application as a decision support tool, the quantified marginal utility of new engaged users can be compared with the cost of obtaining them. This can lead to an estimation, for example, of a metric that takes the form of average minutes saved per dollar spent on promotion. Obviously, this is not a real-life metric, but it can certainly guide business decisions by calculating a form of Return on Investment (ROI) based on it, to decide on the appropriate amount to be spent for promotion, or decide on the duration of promotional activities, depending on the size of the userbase achieved. Moreover, the use of the simulation tool in different geographical areas of interest can help to better target promotional campaigns in locations that lack engagement in order to maximise the impact of the investment. In the case of missing children, the simulation framework demonstrates the improvement margin that public engagement may bring through crowdsourcing initiatives to reduce the principal period between the moment a child is declared missing and the one when it is found. Public engagement in these investigations has already proven to be particularly helpful. From 2018 to 2019, there was an 18% increase in cases resolved with citizens' help, probably signifying that public engagement is rising [90]. The criticism, though, that the Amber Alert system and publicity appeals receive regarding their success in achieving public participation [101; 102] suggests that there is still room for new applications that call for public engagement with better success measuring mechanisms and evidence-based targeting of citizens recruitment.