Agent-based modelling for investigating consumer behaviour in risky markets : the case of food scares

Consumer behaviour in risky markets is a research field which was investigated and which is still investigated from different perspectives. Agent-based modelling is a relatively new research method which offers rich possibilities for consumer research. In this thesis a multi agent simulation was implemented for investigating consumer behaviour in case of food scares. The influence of information regarding the safety of food released by the media is in the centre of the investigations. An artificial consumer population receives this information and discusses about it within their networks. Based on internal learning mechanisms in the sense of Bayesian updating each consumer agent revises its trust with respect to the safety of the food item under investigation. Different information scenarios are tested where the outcomes served as measures of effectiveness. In this way it was possible to test the influence of different information strategies regarding the trust recovery concerning the safety of the food item under investigation.