Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems PROJECT TITLE :Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systemsABSTRACT:How can we model influence between individuals in a social system, even when the network of interactions is unknown? In this article, we review the literature on the “influence model,” which utilizes independent time series to estimate how much the state of one actor affects the state of another actor in the system. We extend this model to incorporate dynamical parameters that allow us to infer how influence changes over time, and we provide three examples of how this model can be applied to simulated and real data. The results show that the model can recover known estimates of influence, it generates results that are consistent with other measures of social networks, and it allows us to uncover important shifts in the way states may be transmitted between actors at different points in time. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Disturbed Bayesian Learning in Multiagent Systems: Improving our understanding of its capabilities and limitations Security of Online Reputation Systems: The evolution of attacks and defenses
