Umeå University's logo

It is very challenging for policymakers and other decision makers to make any kind of decision on a new policy, as the reaction of a person to that policy (policy as one form of a norm) in a given situation is highly individual and based on their own subjective perspective. This becomes even more challenging in environments with a high degree of uncertainty (as is usually the case for policymakers).

Social simulations are a powerful tool for policymakers and other decision makers to support them in their decision-making process. To build agent-based social simulations that provide this support two main challenges exist: norm (policy) realistic behavior and the usability of the simulation.

Norm realistic behavior includes differentiated norm engagement as well as seeing norms as more than just restrictions on behavior. Situated norm engagement means that people react differently to norms and focus only on the parts that are relevant for them. Seeing norms as more than just restrictions on behavior means that people can also violate norms and be motivated to circumvent norms. To address these two parts, we formalize different perspectives on norms and develop a novel agent deliberation architecture, called the Perspective-Based Agent Deliberation Architecture (PBADA) that can represent different perspectives on norms. Another key element of our agent deliberation architecture is that norms are explicit objects.

Having norms as explicit objects is crucial for addressing the challenge of usability of the simulation. It allows policymakers to modify them interactively in the simulation. In general, we see usability as empowering the policy maker to use the simulation in a - for them - meaningful way. Policymakers need to understand how a norm (policy) is influencing the behavior of the agents and in what way. Furthermore, policymakers need to be able to modify existing norms and add new ones on the fly. This requires interaction tools and visualization capabilities necessary to support them in this process. To address this challenge, we present preliminary work on such an interaction tool. 

Social simulations can be a powerful tool for policy makers and other decision makers to support them in their decision making process. To be a powerful tool, it is not only important that the agents in the simulation exhibit realistic–human like—behavior, but also that the simulation is empowering the policy maker to use it in a—for them—meaningful way. To tackle this problem, we require interaction tools and visualization capabilities necessary to support non-expert users (such as policy makers). To understand what these non-expert users need, we performed a focus group study to gain insights into the potential requirements of such an interaction tool with respect to norms. This resulted in a list of requirements to support non-expert users in the analysis of norm conflicts after they happened during the simulation. But more importantly we are calling for a new research agenda in the field of social simulation to support non-expert users with the abstract analysis of norms before conflicts happen in the simulation run, as the results showed that the participants were not able to do that kind of analysis.

Norms influence behaviour in many ways. In situations such as the COVID-19 pandemic where the effect of policies on the spread of the virus is evaluated, this leads to disputes about their effectiveness. In order to build agent-based social simulations that give proper support for this evaluation process we need agents that properly deal with norms. In this paper we present a new agent deliberation architecture that takes more aspects of norms into account than traditional architectures have done. Dealing properly with norms means that agents can reason through the consequences of the norms, that they are used to motivate and not just constrain behaviour, and that the agents can violate the norm as well. For the former we use the ideas of perspectives on norms, while the latter is enabled through the use of values. Within our architecture we can also represent habitual behaviour, context sensitive planning, and through the use of landmarks, reactive planning. We use the example of a restaurant-size based restriction to show how our architecture works.

Norms are a crucial part of human behavior that received a lot of attention within the social simulation community. However, some aspects—up until now—have not been addressed in existing agent architectures, such as their motivational aspects and their importance and impact in planning and action selection. In this paper we present an agent architecture capable of grasping this potential of norms. We use perspectives to reflect how different people engage with a norm, and how it effects their long-term goals, their planning, and course of action. Our architecture is capable of having fast habitual-like behavior, as well as more complex deliberation if necessary.

For agent-based social simulations to be a powerful tool for policy makers and other decision makers in a given context (e.g. the current COVID-19 pandemic), they need to be socially realistic and thus, appropriately represent complex social concepts, such as social rules. In this paper, we focus on norms. Norms describe ‘normal’ behavior and aim at assuring the interests and values of groups or the society as a whole. People react differently to norms, and focus only on the parts that are relevant for them. Furthermore, norms are not only restrictions on behavior, but also trigger new behavior. Seeing a norm only as a restriction on certain behavior misses important aspects and leads to simulations that can be very misleading. Different perspectives need to be incorporated into the simulation to capture the variety of ways different stakeholders react to a norm and how this affects their interaction. We therefore present an approach to include these different perspectives on norms, and their consequences for different people and groups in decision support simulations. A perspective is specified by their goals, actions, effects of those actions, priorities in values, and social affordances. Through modeling perspectives we enable policy makers and other decision makers (the users) to be active in the modeling process and to tailor the simulation to their specific needs, by representing norms as modifiable objects, and providing textual and graphical representations of norms. This provides them with differentiated insights meaningful for the decisions they are faced with. We indicate the requirements for both the simulation platform as well as the agents that follow from our approach. Early explorations of our social simulation are showing the necessity of our approach.

Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.

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.

The COVID-19 virus has led to a world-wide crisis that requires governments and stakeholders to take far-reaching decisions with limited knowledge of their consequences. This paper presents the AS- SOCC model as a valuable decision-support tool for anticipating the consequences of possible measures by considering many interwoven aspects at the individual, group and societal level. Moreover, this paper illustrates how this model can be applied to study the effects of different testing strategies on the spread of the virus and the healthcare system. We found that excluding age groups from random testing was ineffective, while prioritizing test- ing healthcare and education workers was effective, in combination with isolating the household of an infected person.