Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T22:22:06.415Z Has data issue: false hasContentIssue false
  • English
  • Français

Trust and manipulation in social networks

Published online by Cambridge University Press:  11 February 2016

MANUEL FÖRSTER Open the ORCID record for MANUEL FÖRSTER [Opens in a new window] ,
ANA MAULEON  and
VINCENT J. VANNETELBOSCH
MANUEL FÖRSTER
Affiliation:
Department of Economics, University of Hamburg, Germany (e-mail: manuel.foerster@wiso.uni-hamburg.de)
ANA MAULEON
Affiliation:
CEREC, Saint-Louis University – Brussels CORE, University of Louvain, Louvain-la-Neuve, Belgium (e-mail: ana.mauleon@usaintlouis.be, vincent.vannetelbosch@uclouvain.be)
VINCENT J. VANNETELBOSCH
Affiliation:
CEREC, Saint-Louis University – Brussels CORE, University of Louvain, Louvain-la-Neuve, Belgium (e-mail: ana.mauleon@usaintlouis.be, vincent.vannetelbosch@uclouvain.be)
Get access Rights & Permissions [Opens in a new window]

Abstract

We investigate the role of manipulation in boundedly rational opinion dynamics. Agents are subject to persuasion bias and repeatedly communicate with their neighbors in a social network. They can exert effort to manipulate trust in the opinions of others in their favor and update their opinions about some issue of common interest by taking weighted averages of neighbors' opinions. We show that manipulation can connect a segregated society and thus lead to mutual consensus. Second, we show that manipulation fosters opinion leadership; and surprisingly agents with low trust in their own opinion might get more influential even by being manipulated. Finally, comparative simulations reveal that manipulation is beneficial to information aggregation when preferences and abilities for manipulation are homogeneous, but detrimental in case abilities are concentrated at few powerful agents.

Keywords

social networkstrustmanipulationpersuasion biasopinion leadershipconsensusmisinformation
Type
Research Article
Information
Network Science , Volume 4 , Issue 2 , June 2016 , pp. 216 - 243
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acemoglu, D., Dahleh, M., Lobel, I., & Ozdaglar, A. (2011). Bayesian learning in social networks. The Review of Economic Studies, 78 (4), 1201–36.Google Scholar
Acemoglu, D., & Ozdaglar, A. (2011). Opinion dynamics and learning in social networks. Dynamic Games and Applications, 1 (1), 3–49.Google Scholar
Acemoglu, D., Ozdaglar, A. & ParandehGheibi, A. (2010). Spread of (mis)information in social networks. Games and Economic Behavior, 70 (2), 194–227.Google Scholar
Austen-Smith, D., & Wright, J. R. (1994). Counteractive lobbying. American Journal of Political Science, 38 (1), 25–44.CrossRefGoogle Scholar
Axelrod, R. (1997). The dissemination of culture a model with local convergence and global polarization. Journal of Conflict Resolution, 41 (2), 203–26.Google Scholar
Buechel, B., Hellmann, T. & Klößner, S. (2015). Opinion dynamics and wisdom under conformity. Journal of Economic Dynamics and Control, 52, 240–57.CrossRefGoogle Scholar
Chandrasekhar, A., Larreguy, H., & Xandri, J. (2012). Testing models of social learning on networks: Evidence from a framed field experiment. Mimeo, Massachusetts Institute of Technology.Google Scholar
Choi, S., Gale, D., & Kariv, S. (2012). Social learning in networks: A quantal response equilibrium analysis of experimental data. Review of Economic Design, 16 (2–3), 135–57.CrossRefGoogle Scholar
Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3 (01n04), 87–98.CrossRefGoogle Scholar
DeGroot, M. (1974). Reaching a consensus. Journal of the American Statistical Association, 69 (345), 118–21.CrossRefGoogle Scholar
DeMarzo, P., Vayanos, D., & Zwiebel, J. (2003). Persuasion bias, social influence, and unidimensional opinions. Quarterly Journal of Economics, 118 (3), 909–68.Google Scholar
Esteban, J., & Ray, D. (2006). Inequality, lobbying, and resource allocation. The American Economic Review, 96 (1), 257–79.CrossRefGoogle Scholar
Friedkin, N. E. (1991). Theoretical foundations for centrality measures. American journal of Sociology, 96 (6), 1478–504.Google Scholar
Friedkin, N. E., & Johnsen, E. C. (1990). Social influence and opinions. Journal of Mathematical Sociology, 15 (3–4), 193–206.Google Scholar
Golub, B., & Jackson, M. O. (2010). Naïve learning in social networks and the wisdom of crowds. American Economic Journal: Microeconomics, 2 (1), 112–49.Google Scholar
Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence – models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5 (3), 1–33.Google Scholar
Hunter, J. J. (2005). Stationary distributions and mean first passage times of perturbed Markov chains. Linear Algebra and its Applications, 410, 217–43.Google Scholar
Potters, J. & Van Winden, F. (1992). Lobbying and asymmetric information. Public choice, 74 (3), 269–92.Google Scholar
Watts, A. (2014). The influence of social networks and homophily on correct voting. Network Science, 2 (01), 90–106.Google Scholar