The first working package that came out of this project closes a gap in the literature on farsightedness of coalitions. It provides a way to analyse how society makes decisions with long term consequences that have to be adjusted in the future. In this category fall, for instance, the use of new technologies that have not yet been properly regulated in detail. Failing to provide regulation in the future might have disastrous consequences, and people’s belief about their own ability to regulate such technology in the future has a huge impact on their willingness to allow an introduction in the first place.
The second working package sets a benchmark for rational transmission of information in social networks and is the first that does so. Agents have to decide between becoming active in a protest or remaining inactive. They become active only if the number of active agents in the overall society is sufficiently large. However, they cannot observe everybody but only their neighbours in a social network, that is family, friends, colleagues, etc. From the behaviour of those they observe they can deduce what is happening outside their vicinity and use that information for their own decision. This model of social learning provides a theoretical underpinning for stylized facts on revolutions, namely that they are quick and unanticipated. The theoretical findings are illustrated using data on protests, revolutions, and political violence around the globe in a time frame from 1976 to 2014.
