Everyone who is interested is invited to participate, but we would like to ask you to register.
|15.00 – 15.05||Welcome and Introduction|
|15.05 – 16.00||Melika Liporace (University of Tilburg)|
|16.00 – 17.00||Rense Corten (Utrecht University)|
|17.00||Closing and drinks|
Tinbergen Institute Amsterdam
Gustav Mahlerplein 117
1082 MS Amsterdam
University of Tilburg
Persuasion in Random Networks
This paper studies a Bayesian persuasion problem in a connected world. A sender wants to induce receivers to take some action by committing to a signal structure about a payoff-relevant state. I wonder how information provision is affected by a random network when signals are shared among neighbors. Receivers differ in their prior beliefs; the sender wants to persuade some receivers without dissuading the others. I present and characterize novel strategies through which the network is exploited. These strategies can prove useful if the network is sufficiently segregated. In such a case, connectivity can be beneficial to the sender. When some receivers are especially hard to persuade, exploiting the network becomes more attractive. A less informative signal structure which does not exploit the network is however preferred when the other receivers are especially hard to dissuade. Therefore, polarization has ambiguous effect on the informativeness of the optimal signal structure.
How many people do you know? Studies on estimating the size of extended personal networks
The sociological literature on social networks overwhelmingly considers the number of core social contacts. Social networks, however, reach far beyond this small number of social ties. We know little about individual variation in the size of such extended social networks, even though there are strong theoretical reasons to expect that overall network size is important for individual outcomes (e.g., health, finding jobs) as well as for the connectedness of societies as a whole. A key reason for this lack of knowledge is that, due to their very nature, weaker ties in extended networks are difficult to measure using conventional survey methods (e.g., name-generator questions). In this talk, I present two empirical studies that illustrate state-of-the-art methods to estimate extended network size, based on the “network scale-up approach” and on digital footprints. I’ll present results on Dutch adolescents as well as on the Dutch adult population.