April 05, 2024, ESB 2001

In this talk, we present two network games. In the first one, agents’ rewards are the centralities they obtain in participating in a network, where they decide their outgoing links. In the second, agents with heterogeneous expertise are involved in an estimation task. They interact through a French-De Groot influence system and they seek to minimize the variance of the final estimation by modifying their self-confidence weights. We analyze the Nash equilibria for the two games and discuss some learning schemes.

Fabio Fagnani got his Laurea degree in Mathematics from the University of Pisa and Scuola Normale Superiore of Pisa in 1986 and the PhD in Mathematics from the University of Groningen in 1991. He has been Assistant Professor of Mathematical Analysis at the Scuola Normale Superiore during 1991-1998, and in 1997 he held a Visiting Professor position at MIT. Since 1998 he is with the Politecnico of Torino where he is currently (since 2002) Full Professor of Mathematical Analysis. In the period 2012-2019, he has been the head of the Department of Mathematical Sciences of Politecnico di Torino. His current research interests are on the broad topic of dynamics and control over networks: opinion dynamics, inferential distributed algorithms, epidemic models, network games and learning dynamics in games, social and economic applications. He has published over 70 refereed papers on international journals and over 100 on peer-reviewed conference proceedings. In 2017 he was the recipient of the Petar Kokotovic distinguished professorship from the University of California at Santa Barbara (UCSB). In 2019-2020 he got a Leverhulme visiting professorship at Royal Holloway University of London.