IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v181y2024ics0960077924002807.html
   My bibliography  Save this article

Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies

Author

Listed:
  • Kozitsin, Ivan V.

Abstract

Understanding how individuals change their opinions is essential to mitigating the harmful effect of fake news and stopping opinion polarization. The current situation in the field of opinion dynamics models is that we still do not know exactly how social influence changes individuals’ opinions and behavior. The question of which model best describes real social processes remains debatable. The current paper attempts to reconcile this problem. Building upon a relatively simple opinion dynamics model that can capture the key microscopic mechanisms of social influence and affords a tractable description of various complex influence hierarchies of social systems whereby agents may differ in how they are open to influence and how influential they are, we elaborate a framework to find optimal control, which, thus, can be applied to a broad set of opinion dynamics settings. Using a combination of theoretical and computational approaches, we characterize the properties of the optimal control and systematically demonstrate how these algorithms capture stylized examples, in which the key microscopic mechanisms of social influence as well as various influence hierarchies are covered.

Suggested Citation

  • Kozitsin, Ivan V., 2024. "Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002807
    DOI: 10.1016/j.chaos.2024.114728
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924002807
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114728?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lorien Jasny & Joseph Waggle & Dana R. Fisher, 2015. "An empirical examination of echo chambers in US climate policy networks," Nature Climate Change, Nature, vol. 5(8), pages 782-786, August.
    2. Károly Takács & Andreas Flache & Michael Mäs, 2016. "Discrepancy and Disliking Do Not Induce Negative Opinion Shifts," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-21, June.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2016. "The noisy voter model on complex networks," Papers 1602.06935, arXiv.org, revised Apr 2016.
    5. N. Rashevsky, 1939. "Studies in mathematical theory of human relations. II," Psychometrika, Springer;The Psychometric Society, vol. 4(4), pages 283-299, December.
    6. Xi Chen & Xiao Zhang & Yong Xie & Wei Li, 2017. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    7. N. Rashevsky, 1939. "Studies in mathematical theory of human relations," Psychometrika, Springer;The Psychometric Society, vol. 4(3), pages 221-239, September.
    8. Anastasia Peshkovskaya & Tatiana Babkina & Mikhail Myagkov, 2018. "Social context reveals gender differences in cooperative behavior," Journal of Bioeconomics, Springer, vol. 20(2), pages 213-225, July.
    9. Andreas Flache & Michael Mäs, 2008. "How to get the timing right. A computational model of the effects of the timing of contacts on team cohesion in demographically diverse teams," Computational and Mathematical Organization Theory, Springer, vol. 14(1), pages 23-51, March.
    10. Guillermo Romero Moreno & Sukankana Chakraborty & Markus Brede, 2021. "Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-21, June.
    11. Dino Carpentras & Paul J. Maher & Caoimhe O'Reilly & Michael Quayle, 2022. "Deriving an Opinion Dynamics Model from Experimental Data," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(4), pages 1-4.
    12. Ivan V. Kozitsin, 2023. "Opinion dynamics of online social network users: a micro-level analysis," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 47(1), pages 1-41, January.
    13. Anastasia Peshkovskaya & Tatiana Babkina & Mikhail Myagkov, 2019. "Gender effects and cooperation in collective action: A laboratory experiment," Rationality and Society, , vol. 31(3), pages 337-353, August.
    14. Sinan Aral & Christos Nicolaides, 2017. "Exercise contagion in a global social network," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    15. Yu Zhang & Jason Leezer, 2010. "Simulating human-like decisions in a memory-based agent model," Computational and Mathematical Organization Theory, Springer, vol. 16(4), pages 373-399, December.
    16. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    17. Lorien Jasny & Joseph Waggle & Dana R. Fisher, 2015. "Correction: Corrigendum: An empirical examination of echo chambers in US climate policy networks," Nature Climate Change, Nature, vol. 5(11), pages 1020-1020, November.
    18. Andreas Flache & Michael Mäs & Thomas Feliciani & Edmund Chattoe-Brown & Guillaume Deffuant & Sylvie Huet & Jan Lorenz, 2017. "Models of Social Influence: Towards the Next Frontiers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-2.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weimer, Christopher W. & Miller, J.O. & Hill, Raymond R. & Hodson, Douglas D., 2022. "An opinion dynamics model of meta-contrast with continuous social influence forces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Andreas Flache, 2018. "About Renegades And Outgroup Haters: Modeling The Link Between Social Influence And Intergroup Attitudes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-32, September.
    3. Tanzhe Tang & Amineh Ghorbani & Flaminio Squazzoni & Caspar G. Chorus, 2022. "Together alone: a group-based polarization measurement," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3587-3619, October.
    4. N. Rashevsky, 1941. "Note on the mathematical theory of interaction of social classes," Psychometrika, Springer;The Psychometric Society, vol. 6(1), pages 43-47, February.
    5. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    6. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. N. Rashevsky, 1940. "Contributions to the mathematical theory of human relations III," Psychometrika, Springer;The Psychometric Society, vol. 5(3), pages 203-210, September.
    8. N. Rashevsky, 1942. "Contributions to the mathematical theory of human relations: V," Psychometrika, Springer;The Psychometric Society, vol. 7(2), pages 117-134, June.
    9. Guillaume Deffuant & Ilaria Bertazzi & Sylvie Huet, 2018. "The Dark Side Of Gossips: Hints From A Simple Opinion Dynamics Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-20, September.
    10. Schweitzer, Frank, 2021. "Social percolation revisited: From 2d lattices to adaptive networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    12. Christian Ganser & Marc Keuschnigg, 2018. "Social Influence Strengthens Crowd Wisdom Under Voting," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-23, September.
    13. Lipiecki, Arkadiusz & Sznajd-Weron, Katarzyna, 2022. "Polarization in the three-state q-voter model with anticonformity and bounded confidence," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    14. N. Rashevsky, 1944. "Contributions to the mathematical theory of human relations viii: Size distribution of cities," Psychometrika, Springer;The Psychometric Society, vol. 9(3), pages 201-215, September.
    15. Luo, Yun & Li, Yuke & Sun, Chudi & Cheng, Chun, 2022. "Adapted Deffuant–Weisbuch model with implicit and explicit opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    16. Tanzhe Tang & Caspar G. Chorus, 2019. "Learning Opinions by Observing Actions: Simulation of Opinion Dynamics Using an Action-Opinion Inference Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(3), pages 1-2.
    17. Deffuant, Guillaume & Roubin, Thibaut, 2023. "Emergence of group hierarchy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    18. N. Rashevsky, 1940. "Contributions to the mathematical theory of human relations. IV," Psychometrika, Springer;The Psychometric Society, vol. 5(4), pages 299-303, December.
    19. Richard Thomas Watson & Kirk Plangger & Leyland Pitt & Amrit Tiwana, 2023. "A Theory of Information Compression: When Judgments Are Costly," Information Systems Research, INFORMS, vol. 34(3), pages 1089-1108, September.
    20. Sylvie Huet & Jean-Denis Mathias, 2018. "Few Self-Involved Agents Among Bounded Confidence Agents Can Change Norms," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-27, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002807. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.