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Trust and Manipulation in Social Networks

We investigate the role of manipulation in a model of opinion formation where agents have opinions about some common question of interest. Agents repeatedly communicate with their neighbors in the social network, can exert some effort to manipulate the trust of others, and update their opinions taking weighted averages of neighbors' opinions. The incentives to manipulate are given by the agents' preferences. We show that manipulation can modify the trust structure and lead to a connected society, and thus, make the society reaching a consensus. Manipulation fosters opinion leadership, but the manipulated agent may even gain influence on the long-run opinions. In sufficiently homophilic societies, manipulation accelerates (slows down) convergence if it decreases (increases) homophily. Finally, we investigate the tension between information aggregation and spread of misinformation. We find that if the ability of the manipulating agent is weak and the agents underselling (overselling) their information gain (lose) overall influence, then manipulation reduces misinformation and agents converge jointly to more accurate opinions about some underlying true state.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2013/13065.pdf
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Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 13065.

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Length: 38 pages
Date of creation: Sep 2013
Date of revision:
Handle: RePEc:mse:cesdoc:13065
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  1. Daron Acemoglu & Asuman E. Ozdaglar, 2010. "Opinion Dynamics and Learning in Social Networks," Levine's Working Paper Archive 661465000000000222, David K. Levine.
  2. Gullberg, Anne Therese, 2008. "Lobbying friends and foes in climate policy: The case of business and environmental interest groups in the European Union," Energy Policy, Elsevier, vol. 36(8), pages 2954-2962, August.
  3. Shachar Kariv & Syngjoo Choi & Douglas Gale, 2007. "Social Learning in Networks: A Quantal Response Equilibrium Analysis of Experimental Data," Levine's Bibliography 843644000000000107, UCLA Department of Economics.
  4. Berno Buechel & Tim Hellmann & Stefan Kloessner, 2012. "Opinion Dynamics under Conformity," Working Papers 469, Bielefeld University, Center for Mathematical Economics.
  5. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2008. "Bayesian Learning in Social Networks," NBER Working Papers 14040, National Bureau of Economic Research, Inc.
  6. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion bias, social influence, and uni-dimensional opinions," LSE Research Online Documents on Economics 454, London School of Economics and Political Science, LSE Library.
  7. Nigar Hashimzade & Jean Hindriks & Gareth D. Myles, 2006. "Solutions Manual to Accompany Intermediate Public Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582694, June.
  8. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, Oxford University Press, vol. 127(3), pages 1287-1338.
  9. FORSTER, Manuel & MAULEON, Ana & VANNETELBOSCH, Vincent, 2013. "Trust and manipulation in social networks," CORE Discussion Papers 2013050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. CAULIER, Jean-François & MAULEON, Ana & VANNETELBOSCH, Vincent, 2013. "Allocation rules for coalitional network games," CORE Discussion Papers 2013032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  11. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
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