Integrating Spatial Intelligence for risk perception in an Agent Based Disease Model

An increasing number of spatial agent based models (ABMs) use artificial intelligence to enhance agents’ decisions. There is a difference between ABMs with pure social intelligence based on information exchange among agents and ABMs with integrated spatial intelligence. Spatial intelligence refers to the fact that agents sense their environment, perform a judgement on the condition of this environment, and change their behaviour based on this judgement. When spatial intelligence is used in ABMs, it often facilitates navigation (human or animal) or adaptation to land cover change. Less implementations are available for assessing risky situation engaging agents’ risk perception. In this paper, we present a model that uses a combination of spatial and social intelligence to simulate disease diffusion. Agents evaluate changes in floating plastic debris in a river combined with personal information and media attention on cholera to decide which water source to use. Cognition of agents with respect to perceiving risk and acting upon it is implemented via two Bayesian Networks. Modelling results are compared with data collected during a Massive Open Online Course. Results of the ABM show a strong decline of the number of disease cases after implementation of artificial intelligence. Results from the survey confirm the fact that people judge quality of water visually, but also show the strong influence of communication on risk perception.