The Spread of Misinformation in Networks with Individual and Social Learning

Speaker: Dr Sebastiano Della Lena

Affiliation: Monash University

Online via Zoom: https://uqz.zoom.us/j/84030975989

Abstract 

This paper studies the spread of misinformation when agents have both individual and social learning. Stubborn agents spread misinformation in the network, and the steady state opinion vector is characterized by a convex combination of the true state of the world and the spreaders' opinions. A new centrality measure, that depends both on agents' positions in the network and on characteristics of individual learning, shows that agents with poor individual learning and high influence are the best channels of misinformation. Moreover, I study the optimal declaration of a spreader of misinformation, and I find an inverted U-shaped relationship between her centrality and the incentives to declare falsehoods and extreme opinions. Finally, if agents rule out the sources of misinformation, the consensus time increases with respect to the network's segregation, whereas the learning time decreases with respect to agents' reliance on their individual learning.

About the presenter's meeting

If you would like to meet with Dr Sebastiano Della Lena, please contact Dr Carlos Oyarzun.