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Optimal influence under observational learning

Author

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  • Nikolas Tsakas

    () (Singapore University of Technology and Design and Universidad Carlos III de Madrid)

Abstract

We study a problem of optimal influence in a society where agents learn from their neighbors. We consider a firm that seeks to maximize the diffusion of a new product whose quality is ex–ante uncertain, to a market where consumers are able to compare the qualities of two alternative products as soon as they observe both of them. The firm can seed the product to a subset of the population and our goal is to find which is the optimal subset to target. We provide a necessary and sufficient condition that fully characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents’ decay centrality, which is a measure that takes into account how close an agent is to others, but in a way that very distant agents are weighted less than closer ones.

Suggested Citation

  • Nikolas Tsakas, 2014. "Optimal influence under observational learning," Gecomplexity Discussion Paper Series 4, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.
  • Handle: RePEc:cst:wpaper:4
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    References listed on IDEAS

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    1. Apesteguia, Jose & Huck, Steffen & Oechssler, Jorg, 2007. "Imitation--theory and experimental evidence," Journal of Economic Theory, Elsevier, vol. 136(1), pages 217-235, September.
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    3. Tsakas, Nikolas, 2017. "Diffusion by imitation: The importance of targeting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 118-151.
    4. Andrea Galeotti & Sanjeev Goyal, 2009. "Influencing the influencers: a theory of strategic diffusion," RAND Journal of Economics, RAND Corporation, vol. 40(3), pages 509-532, September.
    5. Sanjeev Goyal, 2007. "Introduction to Connections: An Introduction to the Economics of Networks," Introductory Chapters, in: Connections: An Introduction to the Economics of Networks, Princeton University Press.
    6. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 595-621.
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    8. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
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    Cited by:

    1. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    2. Tsakas Nikolas, 2019. "On Decay Centrality," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 19(1), pages 1-18, January.
    3. Tsakas, Nikolas, 2017. "Diffusion by imitation: The importance of targeting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 118-151.
    4. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2018. "Strategic Influence in Social Networks," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 29-50, February.

    More about this item

    Keywords

    Social Networks; Targeting; Diffusion; Observational Learning.;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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