Agent-Based Modeling of Human Behavior in Crises: How Does Risk Perception Spread in Heterogeneous Populations?

Understanding how to accurately inform the population about disaster risk is necessary for the well-functioning of our societies, but also for effective preparedness and reduced vulnerability.

Agent-based models of human behaviors in emergencies can be pivotal to provide insights to be used in every phase of the disaster cycle, from prevention, to preparedness, response and mitigation. There is a growing awareness of the potential of simulation models of disasters, with several scholars advocating a more systematic use of computational models and data (Burger, Oz, Kennedy et al 2019; Epstein 2009). However, the use of agent-based modeling of social processes happening before, after and during crises is still quite limited for several reasons (Squazzoni et al, 2021).

Here, we use an agent-based model (ABM) to investigate how risk communication spreads in a population of heterogenous agents. Specifically, we study the emergent behavior of a population of individuals who revise their opinion on the risk of a certain event, based on information received from an institution, processed through individual sensitivity, and discussed with peers. Such a complex process may include several cognitive biases at the individual level, but also network effects at the meso-level. Our ABM encapsulates both these features, thus allowing us to explore by means of computer simulations the impact of these features on the emergent behavior of the population and, ultimately, on how accurately institutional information is received and processed by a population. Such insights can be useful to design empirical studies to test them and, in case of empirical support, to use them to design recommendations for policy decision makers.