2018 | 28 | 2 | 85-108
Article title

Opinion formation in social networks

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A number of selected works on the dynamics of opinions and beliefs in social networks has been discussed. Both Bayesian and non-Bayesian approaches to social learning have been considered, but the analysis has been focused on a simple, tractable and widely used model of updating beliefs – the DeGroot model. The author studied the dynamics of opinions based on the DeGroot model from dif-ferent points of view. First, its attractive features and shortcomings were discussed and then some of its extensions have been presented. These models are based on the DeGroot updating rule, but addition-ally incorporate the possibility of improvements and enrichments of the framework.
Physical description
  • Université catholique de Louvain, CORE & Université Paris 1, Centre d’Economie de la Sorbonne CORE, Voie du Roman Pays 34, B-1348 Louvain-la-Neuve, Belgium, address:
  • ACEMOGLU D., OZDAGLAR A., Opinion dynamics and learning in social networks, Dyn. Games Appl., 2011, 1, 3–49.
  • AXELROD R., The dissemination of culture. A model with local convergence and global polarization, J. Conflict Res., 1997, 41, 203–226.
  • BALLESTER C., CALVO-ARMENGOL A., ZENOU Y., Who’s who in networks. Wanted. The key player, Econometrica, 2006, 74 (5), 1403–1417.
  • BANERJEE A., A simple model of herd behavior, Quarter. J. Econ., 1992, 107 (3), 797–817.
  • BANERJEE A., BREZA E., CHANDRASEKHAR A., MOBIUS M., Naïve learning with uninformed agents, Working Paper, Stanford University, 2016.
  • BORGATTI S.P., MEHRA A., BRASS D., LABIANCA G., Network analysis in the social sciences, Science, 2009, 323, 892–895.
  • BERGER R.L., A necessary and sufficient condition for reaching a consensus using DeGroot’s method, J. Am. Stat. Assoc., 1981, 76, 415–419.
  • BRADLEY R., Reaching a consensus, Soc. Choice Welf., 2007, 29, 609–632.
  • BRAMOULLE Y., GALEOTTI A., ROGERS B.W., The Oxford Handbook of the Economics of Networks, Oxford University Press, 2016.
  • BUECHEL B., HELLMANN T., KLOSSNER S., Opinion dynamics and wisdom under conformity, J. Econ. Dyn. Control, 2014, 52, 240–257.
  • CELEN B., KARIV S., Distinguishing informational cascades from herd behavior in the laboratory, Am. Econ. Rev., 2004, 94 (3), 484–497.
  • CHANDRASEKHAR A., LARREGUY H., XANDRI J.P., Testing models of social learning on networks. Evidence from a lab experiment in the field, Working Paper, 2016, DOI: 10.3386/w21468.
  • DEFFUANT G., NEAU D., AMBLARD F., WEISBUCH G., Mixing beliefs among interacting agents, Adv. Compl. Syst., 2000, 3, 87–98.
  • DEGROOT M.H., Reaching a consensus, J. Am. Stat. Assoc., 1974, 69, 118–121.
  • DEMARZO P.M., VAYANOS D., ZWIEBEL J., Persuasion bias, social influence, and unidimensional opinions, Quart. J. Econ., 2003, 118 (3), 909–968.
  • DUBEY P., GARG R., MEYER B.D., Competing for customers in a social network, J. Dyn. Games, 2014, 1 (3), 377–409.
  • ELLISON G., FUDENBERG D., Rules of thumb for social learning, J. Political Economy, 1993, 101, 612–643.
  • ELLISON G., FUDENBERG D., Word-of-mouth communication and social learning, Quart. J. Econ., 1995, 110, 93–126.
  • FORREST P., The Lehrer/Wagner theory of consensus and the zero weight problem, Synthese, 1985, 62, 75–78.
  • FRIEDKIN N.E., JOHNSEN E.C., Social influence and opinions, J. Math. Soc., 1990, 15, 193–206.
  • FRIEDKIN N.E., JOHNSEN E.C., Social influence networks and opinion change, Adv. Group Proc., 1999, 16, 1–29.
  • GALE D., KARIV S., Bayesian learning in social networks, Games Econ. Behav., 2003, 45, 329–346.
  • GILBOA I., SCHMEIDLER D., Case-based decision theory, Quart. J. Econ., 1995, 110, 605–639.
  • GILBOA I., SCHMEIDLER D., A Theory of Case-based Decisions, Cambridge University Press, 2001.
  • GOLUB B., JACKSON M.O., Naïve learning in social networks and the wisdom of crowds, Am. Econ. J. Microecon., 2010, 2 (1), 112–149.
  • GOYAL S., HEIDARI H., KEARNS M., Competitive contagion in networks. Games Eco. Behav., Forthcoming, 2015, DOI: 10.2139/ssrn.1950644.
  • GRABISCH M., MANDEL A., RUSINOWSKA A., TANIMURA E., Strategic influence in social networks, Math. Oper. Res., Forthcoming, 2017, DOI: 10.1287/moor.2017.0853.
  • HEGSELMANN R., KRAUSE U., Opinion dynamics and bounded confidence models, analysis, and simulations, J. Art. Soc. Soc. Sim., 2002, 5 (3), 1–33.
  • JACKSON M.O., Social and Economic Networks, Princeton University Press, 2008.
  • KRAUSE U., A discrete nonlinear and non-autonomous model of consensus formation, Comm. Diff. Eq, 2000, 227–236.
  • LEHRER K., WAGNER C., Rational Consensus in Science and Society. A Philosophical and Mathematical Study, Philosophical Studies Series in Philosophy, 1981, 24.
  • LIGGETT T.M., Interacting Particle Systems, Springer, New York 1985.
  • LORENZ J., A stabilization theorem for dynamics of continuous opinions, Physica A, 2005, 355, 217–223.
  • NEWMAN M.E.J., Networks. An Introduction, Oxford University Press, 2010.
  • NURMI H., Some properties of the Lehrer–Wagner method for reaching rational consensus, Synthese, 1985, 62, 13–24.
  • RUSINOWSKA A., TAALAIBEKOVA A., Opinion formation and targeting when persuaders have extreme and centrist opinions, Working Paper, Centre d’Economie de la Sorbonne, 2018.
  • SAMUELSON L., Evolutionary Games and Equilibrium Selection, MIT Press, Cambridge 1997.
  • SANDHOLM W., Population Games and Evolutionary Dynamics, MIT Press, Cambridge, 2010.
  • SCHMITT F., Consensus, respect, and weighted averaging, Synthese, 1985, 62, 25–46.
  • YILDIZ E., ACEMOGLU D., OZDAGLAR A., SABERI A., SCAGLIONE A., Binary opinion dynamics with stubborn agents, ACM Trans. Econ. Comp., 2013, 1 (4), 1–30.
  • WAGNER C., Consensus through respect. A model of rational group decision-making, Phil. Stud., 1978, 34, 335–349.
  • WAGNER C., Allocation, Lehrer models, and the consensus of probabilities, Theory Dec., 1982, 14, 207–220.
  • WAGNER C., On the formal properties of weighted averaging as a method of aggregation, Synthese, 1985, 62, 97–108.
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