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Agent-Based Models for Assessing Social Influence Strategies

  • Zachary K. Stine
  • Nitin Agarwal
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Motivated by the increasing attention given to automated information campaigns and their potential to influence information ecosystems online, we argue that agent-based models of opinion dynamics provide a useful environment for understanding and assessing social influence strategies. This approach allows us to build theory about the efficacy of various influence strategies, forces us to be precise and rigorous about our assumptions surrounding such strategies, and highlights potential gaps in existing models. We present a case study illustrating these points in which we adapt a strategy, namely, amplification, commonly employed by so-called ‘bots’ within social media. We treat it as a simple agent strategy situated within three models of opinion dynamics using three different mechanisms of social influence. We present early findings from this work suggesting that a simple amplification strategy is only successful in cases where it is assumed that any given agent is capable of being influenced by almost any other agent, and is likewise unsuccessful in cases that assume agents have more restrictive criteria for who may influence them. The outcomes of this case study suggest ways in which the amplification strategy can be made more robust, and thus more relevant for extrapolating to real-world strategies. We discuss how this methodology might be applied to more sophisticated strategies and the broader benefits of this approach as a complement to empirical methods.

Keywords

Social influence Opinion dynamics Automated information campaigns Social bots 

Notes

Acknowledgements

This research is funded in part by the U.S. National Science Foundation (IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2605, N00014-17-1-2675), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059) and the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock. The authors thank the three anonymous referees whose comments were immensely helpful in improving this paper.

References

  1. 1.
    Metaxas, P.T., Mustafaraj, E.: Social media and the elections. Science 338(6106), 472–473 (2012)ADSCrossRefGoogle Scholar
  2. 2.
    Woolley, S.: Automating power: social bot interference in global politics. First Monday 21(4) (2016)Google Scholar
  3. 3.
    Ferarra, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)CrossRefGoogle Scholar
  4. 4.
    Lazer, D.M.J., et al.: The science of fake news. Science 359(6380), 1094–1096 (2018)ADSCrossRefGoogle Scholar
  5. 5.
    Flache, A., et al.: Models of social influence: towards the next frontiers. JASSS 20(4), 2 (2017)CrossRefGoogle Scholar
  6. 6.
    Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst. 3(1–4), 87–98 (2000)CrossRefGoogle Scholar
  7. 7.
    Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence: models, analysis and simulation. JASSS 5(3) (2002)Google Scholar
  8. 8.
    Hegselmann, R., König, S., Kurz, S., Niemann, C., Rambau, J.: Optimal opinion control: the campaign problem. JASSS 18(3), 18 (2015)CrossRefGoogle Scholar
  9. 9.
    Smaldino, P.E.: Models are stupid, and we need more of them. In: Vallacher, R.R., Nowak, A., Read, S.J. (eds.) Computational Social Psychology. Psychology Press (2017)Google Scholar
  10. 10.
    Edmonds, B.: Different modelling purposes. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Arkansas at Little RockLittle RockUSA

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