The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people's lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

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The method of agent-based modeling is rarely used in social psychology, but has the potential to complement and improve traditional research practices. An agent-based model (ABM) consists of a number of virtual individuals-the "agents"-interacting in an artificial, experimenter-controlled environment. In this article, we discuss several characteristics of ABMs that could prove particularly useful with respect to recent recommendations aimed at countering issues related to the current "replication crisis". We address the potential synergies between planning and implementing an ABM on the one hand, and the endeavor of pre-registration on the other. We introduce ABMs as tools for both the generation and the improvement of theory, testing of hypotheses, and for extending traditional experimental approaches by facilitating the investigation of social processes from the intra-individual all the way up to the societal level. We describe examples of ABMs in social psychology, including a detailed description of the CollAct model of social learning. Finally, limitations and drawbacks of agent-based modeling are discussed. In annex 1 and 2, we provide literature and tool recommendations for getting started with an ABM.

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