Skip to main content
SpringerLink
Log in

Opinion Dynamics and Learning in Social Networks

  • Published:
Dynamic Games and Applications Aims and scope Submit manuscript

Abstract

We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Acemoglu D, Ozdaglar A, ParandehGheibi A (2010) Spread of (mis)information in social networks. Games Econ Behav, forthcoming

  2. Acemoglu D, Como G, Fagnani F, Ozdaglar A (2010) Persistent disagreement in social networks. Working paper

  3. Acemoglu D, Bimpikis K, Ozdaglar A (2010) Communication information dynamics in (endogeneous) networks. LIDS report 2813, working paper

  4. Acemoglu D, Dahleh M, Lobel I, Ozdaglar A (2010) Heterogeneity and social learning. Working paper

  5. Acemoglu D, Dahleh M, Lobel I, Ozdaglar A (2009) Bayesian learning in social networks. LIDS report 2780

  6. Acemoglu D, Chernozhukov V, Yildiz M (2007) Learning and disagreement in an uncertain world. Working paper

  7. Aldous D, Fill J (2002) Reversible Markov chains and random walks on graphs. Monograph in preparation. http://www.stat.berkeley.edu/aldous/RWG/book.html

  8. Allen B (1981) Generic existence of completely revealing equilibria for economies with uncertainty when prices convey information. Econometrica 49(5):1173–1199

    Article  MATH  MathSciNet  Google Scholar 

  9. Deffuant G, Amblard F, Weisbuch G, Faure T (2002) How can extremism prevail? A study based on the relative agreement interaction model. J Artif Soc Soc Simul 5(4)

  10. Bala V, Goyal S (1998) Learning from neighbours. Rev Econ Stud 65(3):595–621

    Article  MATH  Google Scholar 

  11. Banerjee A (1992) A simple model of herd behavior. Q J Econ 107:797–817

    Article  Google Scholar 

  12. Banerjee A, Fudenberg D (2004) Word-of-mouth learning. Games Econ Behav 46:1–22

    Article  MATH  MathSciNet  Google Scholar 

  13. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as information cascades. J Polit Econ 100:992–1026

    Article  Google Scholar 

  14. Binmore K (2008) Rational decisions. Princeton University Press, Princeton

    Google Scholar 

  15. Bisin A, Verdier T (2001) The economics of cultural transmission and the dynamics of preferences. J Econ Theory 97(2):298–319

    Article  MATH  MathSciNet  Google Scholar 

  16. Bisin A, Verdier T (2000) Beyond the melting pot: cultural transmission, marriage, and the evolution of ethnic and religious traits. Q J Econ 115(3):955–988

    Article  Google Scholar 

  17. Blondel VD, Hendrickx JM, Tsitsiklis JN (2009) On Krause’s multi-agent consensus model with state-dependent connectivity. IEEE Trans Autom Control 54(11):2586–2597

    Article  MathSciNet  Google Scholar 

  18. Boyd S, Ghosh A, Prabhakar B, Shah D (2005) Gossip algorithms: design, analysis, and applications. In: Proceedings of IEEE INFOCOM

  19. Boyd R, Richerson PJ (1985) Culture and the evolutionary process. The University of Chicago Press, Chicago

    Google Scholar 

  20. Cavalli-Sforza LL, Feldman MW (1981) Cultural transmission and evolution: a quantitative approach. Princeton University Press, Princeton

    Google Scholar 

  21. Chamley C, Gale D (1994) Information revelation and strategic delay in a model of investment. Econometrica 62:1065–1086

    Article  MATH  MathSciNet  Google Scholar 

  22. Chen Y, Kartik N, Sobel J (2008) Selecting cheap talk equilibria. Econometrica 76(1):117–136

    Article  MATH  MathSciNet  Google Scholar 

  23. Clifford P, Sudbury A (1973) A model for spatial conflict. Biometrika 60:581–588

    Article  MATH  MathSciNet  Google Scholar 

  24. de Condorcet NC (1785) Essai sur l’application de l’analyse à la probabilite des décisions rendues à la pluralite des voix. Imprimerie Royale, Paris

    Google Scholar 

  25. Crawford V, Sobel J (1982) Strategic information transmission. Econometrica 50(6):1431–1451

    Article  MATH  MathSciNet  Google Scholar 

  26. Cripps M, Ely J, Mailath G, Samuelson L (2008) Common learning. Econometrica 76:909–933

    Article  MATH  MathSciNet  Google Scholar 

  27. Deffuant G, Neau D, Amblard F, Weisbuch G (2000) Mixing beliefs among interacting agents. Adv Complex Syst 3:87–98

    Article  Google Scholar 

  28. DeGroot MH, Reaching a consensus. J Am Stat Assoc 69:118–121

  29. DellaVigna S, Kaplan E (2007) The Fox News effect: media bias and voting. Q J Econ 122:1187–1234

    Article  Google Scholar 

  30. DeMarzo PM, Vayanos D, Zwiebel J (2003) Persuasion bias, social influence, and unidimensional opinions. Q J Econ 118(3):909–968

    Article  MATH  Google Scholar 

  31. Ellison G, Fudenberg D (1993) Rules of thumb for social learning. J Polit Econ 101(4):612–643

    Article  Google Scholar 

  32. Ellison G, Fudenberg D (1995) Word-of-mouth communication and social learning. Q J Econ 110:93–126

    Article  MATH  Google Scholar 

  33. Fagnani F, Zampieri S (2008) Randomized consensus algorithms over large scale networks. IEEE J Sel Areas Commun, forthcoming

  34. Farrell J, Rabin M (1996) Cheap talk. J Econ Perspect 10(3):103–118

    Google Scholar 

  35. Fortunato S, Stauffer D (2006) Computer simulations of opinions and their reactions to extreme events. In: Albeverio S, Jentsch V, Kantz H (eds) Extreme events in nature and society, 2005. Springer, Berlin

    Google Scholar 

  36. Foster D, Vohra RV (1997) Calibrated learning and correlated equilibrium. Games Econ Behav 21:40–55

    Article  MATH  MathSciNet  Google Scholar 

  37. Foster D, Vohra RV (1999) Regret in the online decision problem. Games Econ Behav 29:7–35

    Article  MATH  MathSciNet  Google Scholar 

  38. Fudenberg D, Levine DK (1998) The theory of learning in games. MIT Press, Cambridge

    MATH  Google Scholar 

  39. Fudenberg D, Tirole J (1991) Game theory. MIT Press, Cambridge

    Google Scholar 

  40. Gale D, Kariv S (2003) Bayesian learning in social networks. Games Econ Behav 45(2):329–346

    Article  MATH  MathSciNet  Google Scholar 

  41. Galeotti A, Ghiglino C, Squintani F (2010) Strategic information transmission in networks. Working paper

  42. Galton F (1907) Vox populi. Nature 75:450–451

    Article  Google Scholar 

  43. Gilboa I, Postlewaite A, Schmeidler D (2007) Rationality of belief. Or: why Savage’s axioms are neither necessary nor sufficient for rationality. Working paper

  44. Gilboa I, Schmeidler D (1995) Case-based decision theory. Q J Econ 110:605–639

    Article  MATH  Google Scholar 

  45. Gilboa I, Schmeidler D (2001) A theory of case-based decisions. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  46. Gintis H (2009) The bounds of reason: game theory and the unification of the behavioral sciences. Princeton University Press, Princeton

    MATH  Google Scholar 

  47. Gladwell M (2000) The tipping point: how little things can make a big difference. Little Brown

  48. Glauber RJ (1963) Time-dependent statistics of the Ising model. J Math Phys 4:294–307

    Article  MATH  MathSciNet  Google Scholar 

  49. Golosov M, Lorenzoni G, Tsyvinski A (2009) Decentralized trading with private information. Working paper

  50. Golub B, Jackson MO (2010) How homophily affects diffusion and learning in networks. Unpublished manuscript

  51. Golub B, Jackson MO (2007) Naïve learning in social networks and the wisdom of crowds. Am Econ J, Microecon 2(1):112–149

    Article  Google Scholar 

  52. Grossman SJ (1977) The existence of future markets, and my special expectations and informational externalities. Rev Econ Stud 44(2):431–449

    MATH  Google Scholar 

  53. Grossman S, Stiglitz JE (1980) On the impossibility of informationally efficient markets. Am Econ Rev 70(3):393–408

    Google Scholar 

  54. Gul F (1998) A comment on Aumann’s Bayesian view. Econometrica 66(4):923–927

    Article  MATH  MathSciNet  Google Scholar 

  55. Hagenbach J, Koessler F (2010) Strategic communication networks. Working paper

  56. Hart S, Mas-Colell A (2000) A simple adaptive procedure leading to correlated equilibrium. Econometrica 68(5):1127–1150

    Article  MATH  MathSciNet  Google Scholar 

  57. Haviv M, Van Der Heyden L (1984) Perturbation bounds for the stationary probabilities of a finite Markov chain. Adv Appl Probab 16(4):804–818

    Article  MATH  Google Scholar 

  58. Hayek FA (1945) The use of knowledge in society. Am Econ Rev 35(4):519–530

    Google Scholar 

  59. Hegselmann R, Krause U (2002) Opinion dynamics and bounded confidence models, analysis, and simulations. J Artif Soc Soc Simul 5:1–33

    Google Scholar 

  60. Holley R, Liggett TM (1975) Ergodic theorems for weakly interacting systems and the voter model. Ann Probab 3:643–663

    Article  MATH  MathSciNet  Google Scholar 

  61. Jackson MO (2008) Social and economic networks. Princeton University Press, Princeton

    MATH  Google Scholar 

  62. Jadbabaie A, Lin J, Morse S (2003) Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Autom Control 48(6):988–1001

    Article  MathSciNet  Google Scholar 

  63. Krause U (2000) A discrete nonlinear and nonautonomous model of consensus formation. In: Elaydi S, Ladas G, Popenda J, Rakowski J (eds) Communications in difference equations. Gordon and Breach, Amsterdam

    Google Scholar 

  64. Lee I (1993) On the convergence of informational cascades. J Econ Theory 61:395–411

    Article  MATH  Google Scholar 

  65. Liggett TM (1985) Interacting particle systems. Springer, New York

    MATH  Google Scholar 

  66. Lobel I, Ozdaglar A, Feijer D (2010) Distributed multi-agent optimization with state-dependent communication. LIDS report 2834

  67. Lobel I, Ozdaglar A (2010) Distributed subgradient methods for convex optimization over random networks. LIDS report 2800. IEEE Trans Autom Control, to appear

  68. Lorenz J (2005) A stabilization theorem for dynamics of continuous opinions. Physica A 355:217–223

    Article  MathSciNet  Google Scholar 

  69. Mobilia M, Petersen A, Redner S (2007) On the role of zealotry in the voter model. J Stat Mech Theory Exp 128:447–483

    MATH  MathSciNet  Google Scholar 

  70. Morgan J, Stocken P (2008) Information aggregation in polls. Am Econ Rev 98(3):864–896

    Article  Google Scholar 

  71. Nedic A, Ozdaglar A (2009) Distributed subgradient methods for multi-agent optimization. IEEE Trans Autom Control 54(1):48–61

    Article  MathSciNet  Google Scholar 

  72. Olshevsky A, Tsitsiklis JN (2009) Convergence speed in distributed consensus and averaging. SIAM J Control Optim 48(1):33–55

    Article  MATH  MathSciNet  Google Scholar 

  73. Ostrovsky M (2009) Information aggregation in dynamic markets with strategic traders. Working paper

  74. Quine WV (1969) Natural kinds. In: Rescher N (ed) Essays in honor of Carl G. Hempel. Reidel, Dordrecht

    Google Scholar 

  75. Radner R (1972) Existence of equilibrium of plans, prices and price expectations in a sequence of markets. Econometrica 40(2):289–303

    Article  MATH  MathSciNet  Google Scholar 

  76. Radner R (1982) Equilibrium under uncertainty. In: Arrow KJ, Intiligator MD (eds) Handbook of mathematical economics, vol 2. Elsevier, Amsterdam, pp 923–1006

    Google Scholar 

  77. Richerson PJ, Boyd R (2005) Not by genes alone: how culture transformed human evolution. The University of Chicago Press, Chicago

    Google Scholar 

  78. Robinson J (1951) An iterative method of solving a game. Ann Math 54:296–301

    Article  Google Scholar 

  79. Samuelson L (1997) Evolutionary games and equilibrium selection. MIT Press, Cambridge

    MATH  Google Scholar 

  80. Sandholm W (2010) Population games and evolutionary dynamics. MIT Press, Cambridge

    Google Scholar 

  81. Savage LJ (1954) The foundations of statistics. Wiley, New York

    MATH  Google Scholar 

  82. Schmeidler D (1989) Subjective probability and expected utility without additivity. Econometrica 57(2):571–587

    Article  MATH  MathSciNet  Google Scholar 

  83. Schweitzer PJ (1968) Perturbation theory and finite Markov chains. J Appl Probab 5(2):401–413

    Article  MATH  MathSciNet  Google Scholar 

  84. Simon HA (1957) Models of man. Wiley, New York

    MATH  Google Scholar 

  85. Smith L, Sorensen P (1998) Rational social learning with random sampling. Unpublished manuscript

  86. Smith L, Sorensen P (2000) Pathological outcomes of observational learning. Econometrica 68(2):371–398

    Article  MATH  MathSciNet  Google Scholar 

  87. Tahbaz-Salehi A, Jadbabaie A (2008) A necessary and sufficient condition for consensus over random networks. IEEE Trans Autom Control 53(3):791–795

    Article  MathSciNet  Google Scholar 

  88. Tsitsiklis JN (1984) Problems in decentralized decision making and computation. PhD thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

  89. Tsitsiklis JN, Bertsekas DP, Athans M (1986) Distributed asynchronous deterministic and stochastic gradient optimization algorithms. IEEE Trans Autom Control 31(9):803–812

    Article  MATH  MathSciNet  Google Scholar 

  90. Vives X (1997) Learning from others: a welfare analysis. Games Econ Behav 20:177–200

    Article  MATH  MathSciNet  Google Scholar 

  91. Watts D (2003) Six degrees: the science of a connected age. Norton, New York

    Google Scholar 

  92. Weibull J (1995) Evolutionary game theory. MIT Press, Cambridge

    MATH  Google Scholar 

  93. Weisbuch G, Kirman A, Herreiner D (2000) Market organization. Economica 110:411–436

    Google Scholar 

  94. Welch I (1992) Sequential sales, learning and cascades. J. Finance 47:695–732

    Article  Google Scholar 

  95. Wolinsky A (1990) Information revelation in a market with pairwise meetings. Econometrica 58(1):1–23

    Article  MATH  MathSciNet  Google Scholar 

  96. Wu F, Huberman B (2008) How public opinion forms. In: WINE’ 08

Download references

Author information

Authors and Affiliations

  1. Department of Economics, MIT, Cambridge, MA, 02139, USA

    Daron Acemoglu

  2. Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, MIT, Cambridge, MA, 02139, USA

    Asuman Ozdaglar

Authors
  1. Daron Acemoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asuman Ozdaglar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daron Acemoglu.

Additional information

We thank Stefana Stantcheva for excellent research assistance.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Acemoglu, D., Ozdaglar, A. Opinion Dynamics and Learning in Social Networks. Dyn Games Appl 1, 3–49 (2011). https://doi.org/10.1007/s13235-010-0004-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13235-010-0004-1

Keywords

Search

Navigation