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Influencing Opinion Networks: Optimization and Games

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Abstract

We consider a model of influence over a network with finite-horizon opinion dynamics. The network consists of agents that update their opinions via a trust structure as in the DeGroot dynamics. The model considers two potential external influencers that have fixed and opposite opinions. They aim to maximally impact the aggregate state of opinions at the end of the finite horizon by targeting with precision one agent in one specific time period. In the case of only one influencer, we characterize optimal targets on the basis of two features: shift and amplification. Also, conditions are provided under which a specific target is optimal: the maximum-amplification target. In the case of two influencers, we focus on the existence and characterization of pure strategy equilibria in the corresponding two-person strategic zero-sum game. Roughly speaking, if the initial opinions are not too much in favour of either influencer, the influencers’ equilibrium behaviour is also driven by the amplification of targets.

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Notes

  1. Throughout this paper, we adopt the convention that \(W^0=I\), where I denotes the \(n \times n\) identity matrix.

  2. We denote the \(j^{\text {th}}\) unit vector of length n by \(\textbf{e}_j\), where \(\textbf{e}^k_j=1\) if \(k=j\) and \(\textbf{e}^k_j=0\) if \(k \ne j\).

  3. We denote the all-ones vector of length n by \(\textbf{e}\).

References

  1. Bimpikis K, Ozdaglar A, Yildiz E (2016) Competitive targeted advertising over networks. Oper Res 63(3):705–720

    Article  MathSciNet  MATH  Google Scholar 

  2. DeGroot MH (1974) Reaching a consensus. J Am Stat Assoc 69(345):118–121

    Article  MATH  Google Scholar 

  3. del Pozo M, Manuel C, Gonzalez-Aranguena E, Owen G (2011) Centrality in directed social networks: a game theoretic approach. Social Netw 33(3):191–200

    Article  MATH  Google Scholar 

  4. French JJ (1956) A formal theory of social power. Psychol Rev 63:181–194

    Article  Google Scholar 

  5. Goyal S, Heidari H, Kearns M (2014) Competitive contagion in networks. Games Econ Behav 113:58–79

    Article  MathSciNet  MATH  Google Scholar 

  6. Grabisch M, Li F (2020) Anti-conformism in the threshold model of collective behavior. Dyn Games Appl 10:444–477

    Article  MathSciNet  MATH  Google Scholar 

  7. Grabisch M, Mandel A, Rusinowska A, Tanimura E (2018) Strategic influence in social networks. Math Oper Res 43(1):29–50

    Article  MathSciNet  MATH  Google Scholar 

  8. Grabisch M, Poindron A, Rusinowska A (2019) A model of anonymous influence with anti-conformist agents. J Econ Dyn Control 109:103773

    Article  MathSciNet  MATH  Google Scholar 

  9. Hunter D, Zaman T (2022) Optimizing opinions with stubborn agents. Oper Res 70(4):2119–2137

    Article  MathSciNet  MATH  Google Scholar 

  10. Husslage B, Borm P, Burg T, Hamers H, Lindelauf R (2015) Ranking terrorists in networks: a sensitivity analysis of al qaeda’s 9/11 attack. Social Netw 42:1–7

    Article  Google Scholar 

  11. Lever C (2010) Strategic competitions over networks. PhD thesis, Stanford University, Stanford, CA

  12. Nash J (1951) Non-cooperative games. Ann Math 54(2):286–295

    Article  MathSciNet  MATH  Google Scholar 

  13. Proskurnikov AV, Tempo R (2017) A tutorial on modeling and analysis of dynamic social network, part i. Ann Rev Control 43:65–79

    Article  Google Scholar 

  14. Proskurnikov AV, Tempo R (2018) A tutorial on modeling and analysis of dynamic social network: part ii. Ann Rev Control 45:166–190

    Article  MathSciNet  Google Scholar 

  15. Russell M (2022) Foreign interference in eu democratic processes. Technical Report PE 729.271, Special Committee on Foreign Interference

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Correspondence to Wout de Vos.

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de Vos, W., Borm, P. & Hamers, H. Influencing Opinion Networks: Optimization and Games. Dyn Games Appl (2023). https://doi.org/10.1007/s13235-023-00543-6

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