Volume 16 Number 2
April 2019
Article Contents
Brian D. O. Anderson and Mengbin Ye. Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks. International Journal of Automation and Computing, vol. 16, no. 2, pp. 129-149, 2019. doi: 10.1007/s11633-019-1169-8
Cite as: Brian D. O. Anderson and Mengbin Ye. Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks. International Journal of Automation and Computing, vol. 16, no. 2, pp. 129-149, 2019.

# Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks

Author Biography:
• Brian D. O. Anderson received the B. Sc. degree in pure mathematics in 1962, and B. Eng. degree in electrical engineering in 1964, from the University of Sydney, Australia, and the Ph. D. degree in electrical engineering from Stanford University, USA in 1966. He is an emeritus professor at the Australian National University (having retired as distinguished professor in 2016), distinguished professor at Hangzhou Dianzi University, and distinguished researcher in Data61-CSIRO, Australia. 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 Eidgenoessiche Technische Hochschule (Swiss Federal Institute of Technology), Zurich. He is a past president of the International Federation of Automatic Control and the Australian Academy of Science. His research interests include distributed control and econometric modelling. E-mail: Brian.Anderson@anu.edu.au (Corresponding author) ORCID iD: 0000-0002-1493-4774

Mengbin Ye received the B. Eng. degree (with First Class Honours) in mechanical engineering from University of Auckland, New Zealand in 2013, and the Ph. D. degree in engineering at the Australian National University, Australia in 2018. He is currently a postdoctoral researcher with the Faculty of Science and Engineering, University of Groningen, the Netherlands. His research interests include opinion dynamics and social networks, consensus and synchronisation of Euler-Lagrange systems, and localisation using bearing measurements. E-mail: m.ye@rug.nl ORCID iD: 0000-0003-1698-0173

• Accepted: 2018-12-29
• Published Online: 2019-02-02
• A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create " pluralistic ignorance”, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals.
•  [1] Farzam Matinfar. A Computational Model for Measuring Trust in Mobile Social Networks Using Fuzzy Logic . International Journal of Automation and Computing, 2020, 17(6): 812-821.  doi: 10.1007/s11633-020-1232-5 [2] Kai Li, Tong Xu, Shuai Feng, Li-Sheng Qiao, Hua-Wei Shen, Tian-Yang Lv, Xue-Qi Cheng, En-Hong Chen. The Propagation Background in Social Networks: Simulating and Modeling . International Journal of Automation and Computing, 2020, 17(3): 353-363.  doi: 10.1007/s11633-020-1227-2 [3] Biao Chang, Tong Xu, Qi Liu, En-Hong Chen. Study on Information Diffusion Analysis in Social Networks and Its Applications . International Journal of Automation and Computing, 2018, 15(4): 377-401.  doi: 10.1007/s11633-018-1124-0 [4] Heitor J. Savino, Fernando O. Souza, Luciano C. A. Pimenta. Consensus on Intervals of Communication Delay . International Journal of Automation and Computing, 2018, 15(1): 13-24.  doi: 10.1007/s11633-017-1095-6 [5] Lei Zou, Zi-Dong Wang, Dong-Hua Zhou. Event-based Control and Filtering of Networked Systems: A Survey . International Journal of Automation and Computing, 2017, 14(3): 239-253.  doi: 10.1007/s11633-017-1077-8 [6] Xiao-Long Xu, Nik Bessis, Peter Norrington. Hybrid Collaborative Management Ring on Mobile Multi-agent for Cloud-P2P . International Journal of Automation and Computing, 2016, 13(6): 541-551.  doi: 10.1007/s11633-016-1002-6 [7] Zhi-Chao Song,  Yuan-Zheng Ge,  Hong Duan,  Xiao-Gang Qiu. Agent-based Simulation Systems for Emergency Management . International Journal of Automation and Computing, 2016, 13(2): 89-98.  doi: 10.1007/s11633-016-0958-6 [8] Zhen-Hong Yang, Yang Song, Min Zheng, Wei-Yan Hou. Consensus of Multi-agent Systems Under Switching Agent Dynamics and Jumping Network Topologies . International Journal of Automation and Computing, 2016, 13(5): 438-446.  doi: 10.1007/s11633-016-0960-z [9] Lin-Lin Ou,  Jun-Jie Chen,  Dong-Mei Zhang,  Wei-Dong Zhang. Distributed H∞ PID Feedback for Improving Consensus Performance of Arbitrary-delayed Multi-agent System . International Journal of Automation and Computing, 2014, 11(2): 189-196.  doi: 10.1007/s11633-014-0780-y [10] Jessica Davies, Roger Dixon, Roger M. Goodall, Thomas Steffen. Multi-agent Control of High Redundancy Actuation . International Journal of Automation and Computing, 2014, 11(1): 1-9.  doi: 10.1007/s11633-014-0759-8 [11] Ping-Ping Dai,  Cheng-Lin,  Liu Fei Liu. Consensus Problem of Heterogeneous Multi-agent Systems with Time Delay under Fixed and Switching Topologies . International Journal of Automation and Computing, 2014, 11(3): 340-346.  doi: 10.1007/s11633-014-0798-1 [12] Ya-Kun Zhu, Xin-Ping Guan, Xiao-Yuan Luo. Finite-time Consensus for Multi-agent Systems via Nonlinear Control Protocols . International Journal of Automation and Computing, 2013, 10(5): 455-462.  doi: 10.1007/s11633-013-0742-9 [13] R. P. Yadav, P. V. Varde, P. S. V. Nataraj, P. Fatnani, C. P. Navathe. Model-based Tracking for Agent-based Control Systems in the Case of Sensor Failures . International Journal of Automation and Computing, 2012, 9(6): 561-569 .  doi: 10.1007/s11633-012-0680-y [14] Zhong-Qiang Wu,  Yang Wang. Dynamic Consensus of High-order Multi-agent Systems and Its Application in the Motion Control of Multiple Mobile Robots . International Journal of Automation and Computing, 2012, 9(1): 54-62.  doi: 10.1007/s11633-012-0616-6 [15] Hong-Yong Yang,  Fu-Cai Wang,  Si-Ying Zhang. Consensus of Second-order Multi-agent Systems with Nonsymmetric Interconnection and Heterogeneous Delays . International Journal of Automation and Computing, 2011, 8(4): 421-428.  doi: 10.1007/s11633-011-0599-8 [16] Jing Yan, Xin-Ping Guan, Xiao-Yuan Luo, Fu-Xiao Tan. Target Tracking and Obstacle Avoidance for Multi-agent Networks with Input Constraints . International Journal of Automation and Computing, 2011, 8(1): 46-53.  doi: 10.1007/s11633-010-0553-1 [17] Jing Yan, Xin-Ping Guan, Fu-Xiao Tan. Target Tracking and Obstacle Avoidance for Multi-agent Systems . International Journal of Automation and Computing, 2010, 7(4): 550-556.  doi: 10.1007/s11633-010-0539-z [18] Hai-Tao Zhang, Fang Yu, Wen Li. Step-coordination Algorithm of Traffic Control Based on Multi-agent System . International Journal of Automation and Computing, 2009, 6(3): 308-313.  doi: 10.1007/s11633-009-0308-z [19] Xian-Ming Tang,  Jin-Shou Yu. Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload . International Journal of Automation and Computing, 2008, 5(4): 389-394.  doi: 10.1007/s11633-008-0389-0 [20] Hui Yu,  Yong-Ji Wang. Stable Flocking Motion of Mobile Agents Following a Leader in Fixed and Switching Networks . International Journal of Automation and Computing, 2006, 3(1): 8-16.  doi: 10.1007/s11633-006-0008-x
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (7)  / Tables (1)

## Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks

###### 1. Research School of Engineering, Australian National University, Canberra 2601, Australia2. School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China3. Data61-Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia4. Faculty of Science and Engineering, Engineering and Technology Institute Groningen (ENTEG), University of Groningen, Groningen 9747 AG, The Netherland

Abstract: A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create " pluralistic ignorance”, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals.

Brian D. O. Anderson and Mengbin Ye. Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks. International Journal of Automation and Computing, vol. 16, no. 2, pp. 129-149, 2019. doi: 10.1007/s11633-019-1169-8
 Citation: Brian D. O. Anderson and Mengbin Ye. Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks. International Journal of Automation and Computing, vol. 16, no. 2, pp. 129-149, 2019.
Reference (88)

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