We present work that uses agent-based modeling to represent both intra- and inter-personal interactions to evaluate strategies for message targeting aimed at optimizing technology adoption within a social network. Our work demonstrates the advantage afforded by considering both network (structural) and agent (cognitive) properties of participants in the network when creating and targeting messages for propagation within a network to influence adoption behavior. Using previous empirical work in technology adoption, we discuss an approach that demonstrates the interaction effects of structural and cognitive measurements when used for targeted messaging.
['Rudolph Louis Mappus', 'Erica Briscoe', 'Clayton Hutto']
Optimized Influence Targeting for Adoption in Social Networks