Emergency Decision Making and Disaster Recovery

There is growing evidence that the number and severity of natural disasters and their cascading events such as power blackouts are increasing. These extreme events threaten human lives, displace hundreds of thousands of people and cause huge financial losses. Therefore, it is important to understand better how socio-economic systems can best respond to these disasters and how they can recover quickly, build back better and become more resilient.
This thesis comprises five separate studies of four different types of disasters. The overall objective is to improve the understanding of how society copes with and makes decisions in crisis and emergency situations, and how disaster affected areas recover, particularly in terms of speed and quality. This is a huge subject and rather than focusing on just one event or a single type of disaster, the objective is to look at different types of disaster events by studying people’s risk perception and their (real or expected) disaster behaviour in the context of different phases of the disaster cycle from immediate response to longer-term recovery and resilience building.
The five studies featured in this thesis are: 1. ... mehrBehaviour during a long-lasting blackout in France and Germany, investigated through role-playing scenario exercises to study how society would cope. The aim is provide information to emergency managers and policy makers about community needs and people’s likely behaviour in future blackouts, 2. Analyses of people’s preparedness, perception and behaviour during floods in the UK and Germany and their attitude to public authorities, investigated through face-to-face interview surveys with people living and working in the flood prone areas, 3. Analyses of flood evacuation compliance, from both decision-theoretic and game-theoretic perspectives, using the Warning Compliance Model, which incorporates a Bayesian information system that formalizes the statistical effects of a warning forecast based on the harmonious structure of a Hidden Markov Model, 4. Examining recovery after two major comparable floods in UK and Germany in terms of the impacts, levels of preparedness and government response, investigated with face-to-face interview surveys with residents and businesses and online surveys with experts, 5. Tourist destination recovery in the Philippines after earthquake and typhoon, investigated through interviews with tourist managers and stakeholders.
The key areas for future research revolve around identifying in more detail and with greater precision those factors that predispose a society to respond effectively to a disaster, to recover as quickly as possible and to build resilience in order to better confront future disasters.