IJAC Seminar Invitation | February 4, 2021

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Beyond consensus and polarisation: complex social phenomena in social networks 



14:00-15:00 (Beijing/Perth)

17:00-18:00 (Canberra)

February 4, 2021


Zoom Invitation:


Meeting ID655 0982 4178



Brian D. O. Anderson
Research School of Engineering, Australian National University, Canberra, Australia

Bio: Brian Anderson was born in Sydney, Australia. He received the B.Sc. degree in pure mathematics in 1962, and B.E. in electrical engineering in 1964, from Sydney University, Sydney, Australia, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA, in 1966.

He is an Emeritus Professor at the Australian National University. His awards include the IEEE Control Systems Award of 1997, the 2001 IEEE James H Mulligan, Jr Education Medal, and the Bode Prize of the IEEE Control System Society in 1992, as well as several IEEE and other best paper prizes. He is a Fellow of the Australian Academy of Science, the Australian Academy of Technological Sciences and Engineering, the Royal Society, and a foreign member of the US National Academy of Engineering. He holds honorary doctorates from a number of universities, including Université Catholique de Louvain, Belgium, and ETH, Zürich. He is a past president of the International Federation of Automatic Control and the Australian Academy of Science. His current research interests are in distributed control, sensor networks and social networks.


Mengbin Ye 
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia

Bio: Mengbin Ye was born in Guangzhou, China. He received the B.E. degree (with First Class Honours) in mechanical engineering from the University of Auckland, Auckland, New Zealand in 2013, and the Ph.D. degree in systems and control engineering at the Australian National University, Canberra, Australia in 2018. From 2018-2020, he was a postdoctoral researcher with the Faculty of Science and Engineering, University of Groningen, Netherlands. Since 2020, he has been an Optus Fellow at the Optus--Curtin Centre of Excellence in Artificial Intelligence, Curtin University, Perth, Australia.

He has been awarded the 2018 J.G. Crawford Prize (Interdisciplinary), ANU's premier award recognising graduate research excellence. He has also received the 2019 Springer PhD Thesis Prize, and was Highly Commended in the Best Student Paper Award at the 2016 Australian Control Conference. His current research interests include opinion dynamics and decision making in complex social networks, epidemic modelling, coordination of multi-agent systems, and localisation using bearing measurements.



Mathematical models of opinion dynamics on social networks grew from the seminal works by French and Harary (1950s) and DeGroot (1970s). Since then, the development and analysis of such models has received extensive interest from a broad range of scientific communities, including sociology, computer science, physics, economics, and engineering. Many efforts have focused on developing models and identifying conditions that can explain various social phenomena, such as a group of individuals reaching a consensus, becoming polarised, or breaking into clusters.


In this talk, we review three recent developments in modelling complex social phenomena using opinion dynamics models. First, we introduce a model in which an individual may express an opinion different from his/her true opinion due to a pressure to conform to the expressed opinions of others. Besides theoretical results, we use the model to revisit and explain famed Asch conformity experiments from the 1950s. Second, we present the DeGroot-Friedkin model of the evolution of social power, and provide convergence results when the network topology is time-varying. Last, we examine a model in which multiple logically interdependent topics are simultaneously discussed. We show that the opinions of the individuals can converge to a state of persistent disagreement as a consequence of individuals having different understandings of the logical interdependency relations between topics.



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