Yanshan Liu, Vladimir V. Mazalov, Hongwei Gao, Yajin Chen. Inverse Dynamic Game Model of Opinion Dynamics in a Social Network with Stubborn Agents ... P. 130-147

The article studies consensus formation problems in dynamic models of opinion dynamics in social networks with stubborn agents, which include external players influencing the agents’ opinions. The goal of each player is to maintain the agents’ opinion as close as possible to a given target value, where these values may differ for different players. The dynamics in agents’ opinions is described by a system of linear difference equations, and the players’ payoff functions have a quadratic form. The considered dynamic game belongs to the class of linear-quadratic games in discrete time. To find the optimal control at each stage, the Bellman equation is used, which allows one to determine the optimal trajectory of opinion dynamics and an analytical expression for the Nash equilibrium. When the degree of stubbornness of agents is unknown, the article proposes a new approach that uses two-level optimization and then the Euler equations to solve the inverse dynamic game problem based on observed data, which allows one to determine the levels of stubbornness of agents. Numerical experiments demonstrate the influence of the degree of stubbornness of agents and various structures of network interaction on the consensus formation process.

Keywords: opinion dynamics, social network, Bellman equation, inverse dynamic game

Received September 22, 2025

Revised December 01, 2025

Accepted December 15, 2025

Funding Agency: The research of the first and fourth authors was supported by the National Natural Science Foundation of China (No. 72171126) and Systems Science Plus Joint Research Program of Qingdao University (No. XT2024301).

Yanshan Liu, PhD student, Researcher, School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong, 266071 China, e-mail: 2021020269@qdu.edu.cn

Vladimir V. Mazalov, PhD, Prof., Saint Petersburg State University, Saint Petersburg, 198504 Russia; Institute of Applied Mathematical Research, Karelian Scientific Centre RAS, Petrozavodsk, 185910, Russia, e-mail: v.v.mazalov@spbu.ru

Hongwei Gao, PhD, Prof., School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong, 266071 China, e-mail: gaohongwei@qdu.edu.cn

Yajin Chen, PhD student, Researcher, School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong, 266071, China, e-mail: chenyajin@qdu.edu.cn

REFERENCES

1.   Anderson B.D.O., Ye M. Recent advances in the modelling and analysis of opinion dynamics on influence networks. Int. J. Autom. Comput., 2019, vol. 16, no. 2, pp. 129–149. https://doi.org/10.1007/s11633-019-1169-8

2.   Chen J., Li Y., Kou G., Wang H. Effect of three-stage cascade of opinion dynamics models in coupled networks. Neurocomputing, 2024, vol. 572, art. no. 127176. https://doi.org/10.1016/j.neucom.2023.127176

3.   Dong Y., Zhan M., Kou G., Ding Z., Liang H. A survey on the fusion process in opinion dynamics. Inf. Fusion, 2018, vol. 43, pp. 57–65. https://doi.org/10.1016/j.inffus.2017.11.009

4.   DeGroot M.H. Reaching a consensus. J. Amer. Stat. Assoc., 1974, vol. 69, no. 345, pp. 118–121. https://doi.org/10.1080/01621459.1974.10480137

5.   Berger R.L. A necessary and sufficient condition for reaching a consensus using DeGroot’s method. J. Amer. Stat. Assoc., 1981, vol. 76, no. 374, pp. 415–418. https://doi.org/10.1080/01621459.1981.10477662

6.   Friedkin N.E., Johnsen E.C. Social influence and opinions. J. Math. Sociol., 1990, vol. 15, no. 3–4, pp. 193–206. https://doi.org/10.1080/0022250X.1990.9990069

7.   Hegselmann R., Krause U. Opinion dynamics and bounded confidence: models, analysis and simulation. J. Artif. Soc. Soc. Simul., 2002, vol. 5, no. 3, art. no. 2. http://jasss.soc.surrey.ac.uk/5/3/2.html

8.   Shi X., Cao J., Wen G., Perc M. Finite-time consensus of opinion dynamics and its applications to distributed optimization over digraph. IEEE Trans. Cybern., 2018, vol. 49, no. 10, pp. 3767–3776. https://doi.org/10.1109/TCYB.2018.2850765

9.   Zhan M., Kou G., Dong Y., Chiclana F., Herrera-Viedma E. Bounded confidence evolution of opinions and actions in social networks. IEEE Trans. Cybern., 2021, vol. 52, no. 7, pp. 7017–7028. https://doi.org/10.1109/TCYB.2020.3043635

10.   Zou Y., Meng Z. Targeted bipartite consensus of opinion dynamics in social networks with credibility intervals. IEEE Trans. Cybern., 2020, vol. 52, no. 1, pp. 372–383. https://doi.org/10.1109/TCYB.2020.2973422

11.   Jiang H., Mazalov V.V., Gao H., Wang C. Opinion dynamics control in a social network with a communication structure. Dyn. Games Appl., 2023, vol. 13, no. 1, pp. 412–434. https://doi.org/10.1007/s13235-021-00406-y

12.   Stella L., Bagagiolo F., Bauso D., Como G. Opinion dynamics and stubbornness through mean-field games. In: Proc. 52nd IEEE Conf. Decis. Control, IEEE, 2013, pp. 2519–2524. https://doi.org/10.1109/CDC.2013.6760259

13.   Yildiz Y., Özgüler A.B. Opinion dynamics of stubborn agents under the presence of a troll as a differential game. Turk. J. Electr. Eng. Comput. Sci., 2021, vol. 29, no. 7, pp.  3259–3269. https://doi.org/10.3906/elk-2004-50

14.   Jond H.B. Differential game strategies for social networks with self-interested individuals. IEEE Trans. Comput. Soc. Syst., 2024, vol. 11, no. 3, pp. 4426–4439. https://doi.org/10.1109/TCSS.2024.3350736

15.   Engwerda J. LQ Dynamic optimization and differential games. Chichester, John Wiley & Sons, 2005, 512 p. ISBN: 978-0-470-01524-7 .

16.   Basar T., Olsder G.J. Dynamic noncooperative game theory, 2nd ed. Philadelphia, SIAM, 1998, 519 p. ISBN: 9780898714296 .

17.   Asl H.J., Uchibe E. Estimating cost function of expert players in differential games: a model-based method and its data-driven extension. Expert Syst. Appl., 2024, vol. 255, art. no. 124687. https://doi.org/10.1016/j.eswa.2024.124687

18.   Stocco G.F., Cybenko G. Inverse game theory: learning the nature of a game through play. In: Proc. Sensors, Command, Control, Commun., and Intell. (C3I) Technol. Homeland Secur. Homeland Def. XI, Baltimore, Maryland, United States, 2012, vol. 8359, pp. 17–26. https://doi.org/10.1117/12.924756

19.   Lin X., Beling P.A., Cogill R. Multiagent inverse reinforcement learning for two-person zero-sum games. IEEE Trans. Games, 2017, vol. 10, no. 1, pp. 56–68. https://doi.org/10.1109/TCIAIG.2017.2679115

20.   Molloy T.L., Ford J.J., Perez T. Inverse noncooperative dynamic games. IFAC-Papers OnLine, 2017, vol. 50, no. 1, pp. 11788–11793. https://doi.org/10.1016/j.ifacol.2017.08.1989

21.   Köpf F., Inga J., Rothfuп S., Flad M., Hohmann S. Inverse reinforcement learning for identification in linear-quadratic dynamic games. IFAC-Papers OnLine, 2017, vol. 50, no. 1, pp. 14902–14908. https://doi.org/10.1016/j.ifacol.2017.08.2537

22.   Molloy T.L., Ford J.J., Perez T. Finite-horizon inverse optimal control for discrete-time nonlinear systems. Automatica, 2018, vol. 87, pp. 442–446. https://doi.org/10.1016/j.automatica.2017.09.023

23.   Molloy T.L., Garden G.S., Perez T., Schiffner I., Karmaker D., Srinivasan M.V. An inverse differential game approach to modelling bird mid-air collision avoidance behaviours. IFAC-Papers OnLine, 2018, vol. 51, no. 15, pp. 754–759. https://doi.org/10.1016/j.ifacol.2018.09.164

24.   Rothfuß S., Inga J., Köpf F., Flad M., Hohmann S. Inverse optimal control for identification in non-cooperative differential games. IFAC-Papers OnLine, 2017, vol. 50, no. 1, pp. 14909–14915. https://doi.org/10.1016/j.ifacol.2017.08.2538

25.   Molloy T.L., Ford J.J., Perez T. Inverse noncooperative differential games. In: Proc. 2017 IEEE 56th Annu. Conf. Decis. Control (CDC), Melbourne, VIC, Australia, 2017, pp. 5602–5608. https://doi.org/10.1109/CDC.2017.8264504

Cite this article as: Yanshan Liu, Vladimir V.  Mazalov, Hongwei Gao, Yajin Chen. Inverse Dynamic Game Model of Opinion Dynamics in a Social Network with Stubborn Agents. Trudy Instituta Matematiki i Mekhaniki UrO RAN, 2026, vol. 32, no. 2, pp. 130–147.