Modelling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: ‘Keep It Simple, Stupid’. But the community is divided and a new principle appeared: KIDS, ‘Keep It Descriptive, Stupid’. Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as belief, desire and intention (BDI) agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

In recent years, agent-based simulation has become an important tool to study complex systems. However, the models produced are rarely used for decision-making support because stakeholders are o en not involved in the modeling and simulation processes. Indeed, while several tools dedicated to participatory modeling and simulation exist, these are limited to the design of simple KISS models, thus reducing their potential impact. In this article, we present the new participatory tools integrated within the GAMA modeling and simulation platform. These tools, which take advantage of the GAMA platform with respect to the definition of rich KIDS models, allow the modelers to build models graphically and develop distributed serious games easily. Several application examples illustrate their use and potential.