This book contains the proceedings of the 18th Social Simulation Conference (SSC) and covers the state of the art of social simulation modeling. The SSC is the annual conference of the European Social Simulation Association (ESSA) and the major global conference devoted to this topic. It is aimed at promoting social simulation and computational social science. The book is the biggest collection of agent-based modeling research. It covers all aspects of modeling, from theory and philosophy of modeling to question of model design, purpose, and structure, using data, visualization, model interrelation, and open modeling.

This book is targeted at researchers in social simulation regardless of disciplinary backgrounds, across career stages and different sectors, such as academia, industry, and policy

Agent-based models have continuously increased regarding the implementation of the complexity of agent decision making. This poses questions for the ontological foundation of ABM as well as having implications for research design and data collections. This paper situates the meta-modelling framework CAFCA within other contextual and multidimensional agent-architectures, in particular focusing on its contributions to ontology and research design. We also present the challenges for data collection as a consequence of this increased representational complexity.

This chapter provides an overview of and introduction to the proceedings of the Social Simulation Conference 2023. It contains a short intro to social simulation, to the theme of social simulation and public health and the papers accepted from the conference. As a maturing methodology, social simulation has made inroads into substantive disciplines. The conference had a particular focus on applications of social simulation in public health. Over the past decade public health research has embraced social simulation as a method to better understand the population effects of interventions, to model interactions between individual and socio-economic effects on health and to understand the role of social influence in health behaviours. This chapter gives a brief overview of social simulation, its application in public health and provides an overview of the proceedings papers. 

This paper introduces the design intent “meaningful distrust” by analyzing existing gameplay design patterns and complementary theories from the social sciences. To explore meaningful distrust as a design intent, we have analyzed 4 very different games to see what relations and combinations of gameplay design patterns naturally occur, with mappings to relevant theories from the social sciences. This analysis focuses on how the design patterns are combined and how this affects the overall social situation in these games, adding to our understanding of how to design for meaningful distrust.

Agent-based models of human behaviors in emergencies are extremely important in prevention, preparedness, response, and mitigation of crises. However, there is huge variation in the modeling of human cognitions and actions, with varying degrees of realism and even more diverse definitions of how realism should be implemented in the models. The aim of this Rapid Literature Review is to identify existing patterns in modelling realistic behaviours in simulation models of disasters and crises, but also to identify gaps in the literature on the basis of a qualitative assessment of review papers on the topic. We analyze eight papers (identified through a search of 68 papers in the Scopus database) that review ABM models designed either to investigate general behaviors in emergencies (i.e., evacuations), or reactions to specific crises (i.e., COVID-19). Our analysis shows that while all the papers agree in advocating for more realism in modeling human behavior, very little has been done in terms of designing agents, interactions and environments that can be considered realistic.

One of the core assumptions made when building agent-based simulation models is how the agents decide or reason about the action to take next. The mode of reasoning is usually the same for all agents and over time within the simulation run. However, is this adequate? There exist several frameworks that describe multi-mode reasoning, however how do we know what we need? To engage with this core question, we reflect on this modelling process, by using CAFCA—one of these multi-mode frameworks—and reflect on the reasoning dimension in a social dilemma decision situation. More specifically, using existing qualitative inquiry on group dynamics in a common pool resource dilemma—not designed to elicit different types of reasoning—we introduce our hunt for reasoning hints and reflect on what insights/data we would need to make an informed decision about the reasoning(s) in our modelling and how to obtain this.

A major challenge in AI is designing autonomous systems that capture the values of stakeholders, and do so in such away that one can assess the extent to which that system’s behaviour is aligned to those values. In this paper we discuss our response to this challenge that is both practical and built on clear principles. Specifically, we propose eleven heuristics to organise the process of making values operational in the design of particular class of AI systems called online institutions. These are governed systems of interacting communities of human and autonomous artificial agents.

The increasing use of Artificial Intelligence (AI) systems has sparked discussions regarding developing ethically responsible technology. Consequently, various organizations have released high-level AI ethics frameworks to assist in AI design. However, we still know too little about how AI ethics principles are perceived and work in practice, especially in public organizations. This study examines how AI practitioners perceive ethical issues in their work concerning AI design and how they interpret and put them into practice. We conducted an empirical study consisting of semi-structured qualitative interviews with AI practitioners working in or for public organizations. Taking the lens provided by the “In-Action Ethics” framework and previous studies on ethical tensions, we analyzed practitioners’ interpretations of AI ethics principles and their application in practice. We found tensions between practitioners’ interpretation of ethical principles in their work and ‘ethos tensions.’ In this vein, we argue that understanding the different tensions that can occur in practice and how they are tackled is key to studying ethics in practice. Understanding how AI practitioners perceive and apply ethical principles is necessary for practical ethics to contribute toward an empirically grounded, Responsible AI.

Collective action research of natural resource use aims to understand why and when collective overuse arises. Agent-based simulations and behavioural experiments are part of the toolkit for this quest. In most agent-based simulation models however, individual and collective decision-making are discerned, but the crucial transition between these two stances is understudied. In this paper we formalise computational agents able to think and act from an individual, social, or collective stance using a combination of empirical findings and theoretical models on togetherness. To this end, we use a conceptual agent framework to adapt and extend an existing agent-based model designed to advance the understanding of group processes for sustainable governance of dynamic common pool resource environments. The findings of the paper are mainly a conceptual model and future research will further develop the framework as well as the agent-based model for further understanding of the processes involved.

GAM, combining games and agent-based models, shows potential for investigating complex social phenomena. Games offer engaging environments generating insights into social dynamics, perceptions, and behaviours, while agent-based models support the analysis of complexity. Games and agent-based models share the important ability both to input and output qualitative and quantitative data. Currently, there is no overview of GAM approaches. In a systematic literature review, we identified six research design types in empirical studies to date. The functional range of these design types is wide, with diverse application domains involving analogue, digital, and hybrid games. This makes GAM a highly versatile approach, appealing to researchers in both natural and social sciences, along with the gaming community itself. To consolidate the GAM field, we propose recording the design and implementation of studies that combine games and agent-based models by using a dedicated documentation scheme.