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There exists a realism and scalability trade-off in modelling agent-based simulations. As an example, one could create a simpler model with a simpler behavioural model that allows to simulate many agents. However, this simple model can miss important details of the real world. Incorporating these aspects of the real world can increase realism, however, it can come at the cost of scalability. If more aspects of the real world are incorporated into the model, the behavioural model should be tied to these aspects. However, an interdependent behavioural system that considers all information at each time step is usually poorly scalable in terms of deliberative aspects. This poor scalability hinders the expansion of other desirable properties in the model, such as aspects of life and the number of agents.

We propose a context-sensitive deliberation framework that could help increase the scalability of the deliberation without losing behavioural realism. The framework is inspired by Kahneman's concept of thinking fast and thinking slow. On the one hand, it will be capable of fast deliberation that is efficient (scalability). On the other hand, it can sometimes perform slower deliberation that can solve complex situations (behavioural realism). Rather than switching between these two modes, the framework slides, gradually incorporating more information into the deliberation. This is the complexity by need principle. The framework needs to be aware of the context to determine what kind of information to use and what kind of information to deliberate on.

Whether context-sensitive deliberation can increase scalability while retaining realism will be evaluated with a use-case simulation, the Agent-Based Social Simulation of the Coronavirus Crisis (ASSOCC). Context-sensitive deliberation is implemented in the ASSOCC framework. The Original ASSOCC framework is then compared with the context-sensitive ASSOCC variant. The results show that deliberation is no longer the bottleneck, since context-sensitive deliberation achieved a roughly 16-17 times speed-up over the original ASSOCC deliberation model. This speed-up was retained with higher agent numbers, and it can be expected that if deliberation contains more aspects, context-sensitive deliberation will be capable of an even greater speed-up. The behavioural and infection curves were similar between the two models, thus the realism of the model is retained. In conclusion, the work shows that context-sensitive deliberation can increase scalability and retain realism in agent-based simulations.

Inom agentbaserad modellering och simulering finns en inneboende avvägning mellan skalbarhet och realism. Till exempel kan man skapa en simplare modell med en mer simpel beteendemodell, vilket möjliggör simulering av många agenter. En simpel modell kan dock missa viktiga detaljer från den verkliga världen. Att integrera dessa aspekter av verkligheten kan öka realismen, men det kan ske på bekostnad av modellens skalbarheten. Om fler aspekter av verkligheten inkorporeras i modellen, bör beteendemodellen kopplas till dessa aspekter. Ett ömsesidigt beroende beteendesystem som beaktar all information vid varje tidpunkt är dock vanligtvis dåligt skalbart när det gäller deliberativa aspekter. Denna bristande skalbarhet försvårar vidareutvecklingen av andra önskvärda egenskaper i modellen, såsom livsaspekter och antalet agenter.

Vi presenterar ett kontextkänsligt beslutsfattningsramverk som kan bidra till att öka skalbarheten hos beslutsfattningen utan att förlora beteenderealism. Ramverket är inspirerat av Kahneman's system koncept om att tänka snabbt och långsamt. Å ena sidan möjliggör det snabb beslutsfattning som är effektiv (skalbarhet). Å andra sidan kan det ibland utföra ett mer deliberativt beslutsfattande som kan lösa komplexa situationer (beteenderealism). Istället för att växla mellan dessa två lägen, fungerar ramverket med en glidande övergång, där mer information gradvis införlivas i beslutsfattning. Detta kallas för komplexitet-efter-behov-principen. Ramverket måste vara medvetet om kontexten för att kunna avgöra vilken typ av information som ska användas och vad som bör beaktas i beslutsfattning.

Huruvida kontextkänslig beslutsfattning kan öka skalbarheten samtidigt som realismen bibehålls, kommer att utvärderas med hjälp av en användningsfall: Agent-Based Social Simulation of the Coronavirus Crisis (ASSOCC). Ramverket för kontextkänslig beslutsfattning har implementerats i ASSOCC-modellen. Den ursprungliga ASSOCC-modellen jämförs därefter med den kontextkänsliga ASSOCC-varianten. Resultaten visar att beslutsfattning inte längre är flaskhalsen, eftersom kontextkänslig beslutsfattning uppnådde en hastighetsökning på ungefär 16–17 gånger jämfört med det ursprungliga beslutsfattningsmodellen i ASSOCC. Denna hastighetsökning bibehölls vid högre agentantal, och det kan förväntas att om fler aspekter inkluderas i beslutsfattningen, kan kontextkänslig beslutsfattning uppnå ännu större förbättringar. Beteende- och infektionskurvorna var liknande mellan de två modellerna, vilket innebär att modellens realism bevarades. Sammanfattningsvis visar detta arbete att kontextkänslig beslutsfattning kan öka skalbarheten och samtidigt bevara realismen i agentbaserade simuleringar.

Truly realistic models for policy making require multiple aspects of life, realistic social behaviour and the ability to simulate millions of agents. Current state of the art Agent-based models only achieve two of these requirements. Models that prioritise realistic social behaviour are not easily scalable because the complex deliberation takes into account all information available at each time step for each agent. Our framework uses context to considerably narrow down the information that has to be considered. A key property of the framework is that it can dynamically slide between fast deliberation and complex deliberation. Context is expanded based on necessity. We introduce the elements of the framework, describe the architecture and show a proof-of-concept implementation. We give first steps towards validation using this implementation.

Simulating for policy making can require modelling multiple aspects of life, realistic social behaviour and the ability to simulate up to millions of agents [1]. However realistic models are not easily scalable due to the complex deliberation that takes into account all information at every time step which is slow. Explicitly taking into account context in the deliberation can increase scalability, through a complexity by need principle. The Dynamic Context-Sensitive Deliberation (DCSD) framework uses minimal information when possible, but gradually draws in more information when necessary. To validate whether DCSD can increase scalability while retaining realism we implement DCSD into an example large scale model, the Agent-based Social Simulation of the Coronavirus Crisis (ASSOCC). We compare the original deliberation from the ASSOCC model with the implemented DCSD. We conclude that DCSD can increase scalability while retaining realism in large scale social simulation models.

We propose a context-sensitive deliberation framework where the decision context does not deliver an action straight away, but where rather the decision context and agent characteristics influence the type of deliberation and type of information evaluated which will affect the final decision. The framework is based on the Contextual Action Framework for Computational Agents (CAFCA). Our framework also tailors the deliberation type used to the decision context the agent finds itself in, starting from the least cognitive taxing deliberation types unless the context requires more complex deliberation types. As a proof-of-concept the paper shows how context and information relevance can be used to conceptually expand the deliberation system of an agent.

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.

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.

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.

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.