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The general idea of tracking and tracing apps is that they track the contacts of users so that in case a user tests positive for COVID-19, all the other users that she has been in contact with get a warning signal that they have potentially been in contact with the COVID-19 virus. This is, to quarantine potential carriers of the virus even before they show symptoms. We set up a scenario in which we test the effects the introduction of such an app has on the dynamics of infection with varying amounts of app users. Running the experiments resulted in a slightly lower peak of infections for higher app usages and the total amount of infected individuals over the course of the whole run decreased not more than 10% in any case. The app seems mainly effective in decreasing contacts and infections in public spaces (except hospitals) while increasing the contacts and infections at home.

In previous chapters we have described the results and analysis of social simulations for crisis situations based on the experiences of the ASSOCC framework. Whereas we managed to build an implementation in a very short time, based on many years of fundamental research, such an implementation is inherently limited due to the many tasks and challenges that are to be dealt within high time pressure (e.g. keeping up with the many emerging public concerns and specific stakeholder issues with relatively unstable theoretical background while dealing with scaling up technical challenges). This chapter proposes and discusses what can be prepared for future crises, from fundamental conceptual building blocks, to decision components, and technical implementations. Building upon the experience of what we missed and had to create or deal with(out) during the crisis, this chapter is dedicated to point how, as a community, we can be ready to adaptively respond to future crises.

Cocaine trafficking is starting to get modeled by supply chain theory. Supply chain theories are described in many economical papers. These theories are however not directly usable in analyzing illegal supply chains. In this paper we investigate the difference between legal and illegal supply chains. Where the difference of the supply chain lies in two factors, these are trust and risk. Here we model a cocaine trafficking supply chain based on legal supply chain theory. This model will be copied and adjusted with theoretical concepts that are inherent to an illegal supply chain. Comparison of the results of the two models showed that differences in those factors lead to differences in the supply chain, such as clustering and efficiency.

Modelling and simulation approaches are applied in a variety of scientific disciplines for analysing, planning, and optimising complex systems or phenomena. Simulation is not limited to applications related to computer science or information systems research [2] and has also become an accepted method, for example, in social sciences and medicine. But also for solving practical problems, e.g., in manufacturing, logistics, or engineering, the use of simulation is increasingly common. Due to its popularity and versatility, simulation is even referred to as a third pillar of science between theory and experiment [1].

When creating (open) agent systems it has become common practice to use social concepts such as social practices, norms and conventions to model the way the interactions between the agents are regulated. However, in the literature most papers concentrate on only one of these aspects at the time. Therefore there is hardly any research on how these social concepts relate. It is also unclear whether something like a norm evolves from a social practice or convention or whether they are complete independent entities. In this paper we investigate some of the conceptual differences between these concepts. Whether they are fundamentally stemming from a single social object or should be seen as different types of objects altogether. And finally, when one should which type of concept in an implementation or a combination of them.

Simulations created for crises naturally have two important goals: the simulation must both be sound and solid from a scientific standpoint, but also should be exploitable at very short notice by stakeholders in the decision-support of the crisis. A central activity of building simulations during crises is conducting an advanced software project, for which implementing the central simulation model is only one of the many tasks. Taking a systems-design perspective, this chapter describes the needs, concerns, and solutions for achieving the goals raised by simulations during crises by illustrating how they were addressed by the ASSOCC software platform within the project. In particular, ASSOCC goes beyond classic social simulation standards by incorporating dedicated visualisation aspects, leading to an architecture that combines a simulation module (in NetLogo), a visualisation module (in Unity) and an analysis module (in R). This chapter explains what modules were required and for which purpose, what outcome to expect from developing such modules, and how to design and implement such a module and overarching architecture to interact with one another.

This chapter describes how the general theories presented in the previous chapter have been used for the concrete ASSOCC software platform, which is used as the basis for all the scenarios described in Chaps. 5– 10. We will describe the agent architecture and deliberation mechanism based on the needs. We also will introduce the environment which is modelled like a small town in which the agents live. The chapter also describes the epistemiological model that we use to represent the COVID-19 disease specific elements.

In the previous chapters we have described our experiences in simulating for the COVID-19 crisis. We also described how we envision that we can get to a simulation platform that will be more supportive for simulating a next crisis. In this chapter we will recapture a number of the challenges that need to be faced in order to make social simulations grow to their real potential in crisis situations. The challenges range from theory and conceptual models to implementation and use of the simulations. Moreover, we argue that the development of the simulation platform should be accompanied with an effort of the community to connect to established advisory boards and committees in order to integrate the social simulations in the normal set of tools that are used by crisis teams.

Using values as drivers of behavior has already been done in previous research. One of the most well-known universal theories of values is Schwartz’s theory of abstract values. According to his theory, a universal set of abstract values can be imputed to people. As the values used in his system are very abstract, there is a need to translate the abstract values to more concrete values and assign the behavioral choices to them. A theory or methodology for this step has not been developed in a way that is widely applicable. Thus, a precise way of such a translation is necessary for practical purposes. In this paper, we design a practical but formal framework that can be used to study the value-driven behavior of agents in social simulations. We make an agent-based simulation for a fishery village that uses this framework.

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions. Correction available: https://link.springer.com/article/10.1007%2Fs11023-021-09565-8 (WOS:000671651200001)

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.