Review of Economic Design

, Volume 16, Issue 2–3, pp 135–157

Social learning in networks: a Quantal Response Equilibrium analysis of experimental data

Original Paper

Abstract

Individuals living in society are bound together by a social network and, in many social and economic situations, individuals learn by observing the behavior of others in their local environment. This process is called social learning. Learning in incomplete networks, where different individuals have different information, is especially challenging: because of the lack of common knowledge individuals must draw inferences about the actions others have observed, as well as about their private information. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework of Gale and Kariv (Games Econ Behav 45:329–346, 2003) to interpret the data generated by the experiments. The family of three-person networks includes several non-trivial architectures, each of which gives rise to its own distinctive learning patterns. To test the usefulness of the theory in interpreting the data, we adapt the Quantal Response Equilibrium (QRE) model of Mckelvey and Palfrey (Games Econ Behav 10:6–38, 1995; Exp Econ 1:9–41, 1998). We find that the theory can account for the behavior observed in the laboratory in a variety of networks and informational settings. This provides important support for the use of QRE to interpret experimental data.

Keywords

Social networks Social learning Quantal Response Equilibrium (QRE) Experiment 

JEL Classification

D82 D83 C92 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson L, Holt C (1997) Information cascades in the laboratory. Am Econ Rev 87: 847–862Google Scholar
  2. Bala V, Goyal S (1998) Learning from neighbors. Rev Econ Stud 65: 595–621CrossRefGoogle Scholar
  3. Banerjee A (1992) A simple model of herd behavior. Q J Econ 107: 797–817CrossRefGoogle Scholar
  4. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascade. J Polit Econ 100: 992–1026CrossRefGoogle Scholar
  5. Çelen B, Kariv S (2004) Distinguishing informational cascades from herd behavior in the laboratory. Am Econ Rev 94: 484–497CrossRefGoogle Scholar
  6. Çelen B, Kariv S (2005) An experimental test of observational learning under imperfect information. Econ Theory 26: 677–699CrossRefGoogle Scholar
  7. Choi S (2012) A cognitive hierarchy model of learning in networks. Rev Econ Design (this volume)Google Scholar
  8. Choi S, Gale D, Kariv S (2005) Behavioral aspects of learning in social networks: an experimental study. In: Morgan J (ed) Advances in behavioral and experimental economics (the advances in applied microeconomics series). Cambridge University Press, CambridgeGoogle Scholar
  9. Conley T, Udry C (2001) Social learning through networks: the adoption of new agricultural technologies in Ghana. Am J Agric Econ 83: 668–673CrossRefGoogle Scholar
  10. DeMarzo P, Vayanos D, Zwiebel J (2003) Persuasion bias, social influence, and unidimensional opinions. Q J Econ 118: 909–968CrossRefGoogle Scholar
  11. Duflo E, Saez E (2002) Participation and investment decisions in a retirement plan: the influence of colleagues’ choices. J Econ 85: 121–148Google Scholar
  12. Duflo E, Saez E (2003) The role of information and social interactions in retirement plan decisions: evidence from a randomized experiment. Q J Econ 118: 815–842CrossRefGoogle Scholar
  13. Foster A, Rosenzweig M (1995) Learning by doing and learning from others: human capital and technical change in agriculture. J Polit Econ 103: 1176–1209CrossRefGoogle Scholar
  14. Gale D, Kariv S (2003) Bayesian learning in social networks. Games Econ Behav 45: 329–346CrossRefGoogle Scholar
  15. Goeree J, McKelvey R, Palfrey T, Rogers B (2007) Self-correcting information cascades. Rev Econ Stud 74: 733–762CrossRefGoogle Scholar
  16. Goyal S (2005) Learning in networks: a survey. In: Demange G, Wooders M (eds) Group formation in economics: networks, clubs and coalitions. Cambridge University Press, CambridgeGoogle Scholar
  17. Griliches Z (1957) Hybrid corn: an exploration in the economics of technological change. Econometrica 25: 501–522CrossRefGoogle Scholar
  18. Hung A, Plott C (2001) Information cascades: replication and an extension to majority rule and conformity-rewarding institutions. Am Econ Rev 91: 1508–1520CrossRefGoogle Scholar
  19. Jackson M (2005) A survey of models of network formation: stability and efficiency. In: Demange G, Wooders M (eds) Group formation in economics: networks, clubs, and coalitions. Cambridge University Press, CambridgeGoogle Scholar
  20. Jackson M (2008) Social and economic networks. Princeton University Press, PrincetonGoogle Scholar
  21. Kosfeld M (2004) Networks in the laboratory: a survey. Rev Netw Econ 3: 20–41CrossRefGoogle Scholar
  22. Kübler D, Weizsäcker G (2003) Limited depth of reasoning and failure of cascade formation in the laboratory. Rev Econ Stud 71: 425–441Google Scholar
  23. Manski C (1993) Identifcation of exogenous social effects: the reection problem. Rev Econ Stud 60: 531–542CrossRefGoogle Scholar
  24. Manski C (1995) Identifcation problems in the social sciences. Harvard University Press, CambridgeGoogle Scholar
  25. Mckelvey R, Palfrey T (1995) Quantal response equilibria for extensive form games. Games Econ Behav 10: 6–38CrossRefGoogle Scholar
  26. Mckelvey R, Palfrey T (1998) Quantal response equilibria for extensive form games. Exp Econ 1: 9–41Google Scholar
  27. Mueller-Frank M (2011) A general framework for rational learning in social networks. Theor Econ (forthcoming)Google Scholar
  28. Munshi K (2004) Social learning in a heterogeneous population: technology diffusion in the Indian green revolution. J Dev Econ 73: 185–213CrossRefGoogle Scholar
  29. Pagan A, Ullah A (1999) Nonparametric econometrics. Cambridge University Press, CambridgeGoogle Scholar
  30. Rosenberg D, Solan E, Vieille N (2009) Informational externalities and emergence of consensus. Games Econ Behav 66: 979–994CrossRefGoogle Scholar
  31. Smith L, Sørensen P (2000) Pathological outcomes of observational learning. Econometrica 68: 371–398CrossRefGoogle Scholar
  32. Weizsäcker G (2010) Do we follow others when we should? A simple test of rational expectations. Am Econ Rev 100: 2340–2360CrossRefGoogle Scholar
  33. Zheng JX (1996) A consistent test of functional form via nonparametric estimation techniques. J Econ 75: 263–289Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of EconomicsUniversity College LondonLondonUK
  2. 2.Department of EconomicsNew York UniversityNew YorkUSA
  3. 3.Department of EconomicsUniversity of CaliforniaBerkeleyUSA

Personalised recommendations