Many individuals experience problems understanding and preparing for low-probability  impact risk, like natural disasters and pandemics – unless they experience these events, yet then it is often too late to avoid damages. Individuals with recent disaster risk experience are, on average, better prepared. This seems to be mediated through emotions and a better understanding of the consequences.

Researchers from the Institute for Environmental Studies (IVM) at VU Amsterdam are interested in the effects of a virtual reality experience of flooding, which is one of the deathliest and most damaging natural disasters in the world. They are the first to test whether such an experience can stimulate people to invest in risk reducing measures in the context of flooding. In the VR experience, participants are first asked to stack sandbags to protect their home from flooding. After finishing the Sandbag task, participants can freely walk around their Protected living room. In a final stage, participants may explore the unprotected living room of the Neighbors.

The project investigates the possibility to boost risk perception, coping appraisal, negative emotions and damage-reducing behavior through a simulated flooding experience. An important novelty of the current set-up is that the authors examine the persistence of the effects by using a follow-up survey several weeks after the VR intervention.

Jantsje Mol enlisted the help of the Tech Labs of the Network Institute to develop an interactive Virtual Environment (VE) to test these ideas. The VE was built over the coarse of several months and includes realistic waterflows, two houses and several tasks to be performed by the participants.

The experiment was conducted in the Network Institute Tech Labs in October 2020 with more than 100 participants living in and around Amsterdam, aged 18 – 88 years. The experiment was conducted under a strict Corona Protocol and was approved by the Ethical Review Committee of the Faculty of Science (protocol #200507). The project is currently in the data analysis stage.