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Diffusion by Imitation: The Importance of Targeting Agents

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Listed author(s):
  • Nikolas Tsakas

Abstract

We study the optimal targeting strategy of a planner who seeks to maximize the diffusion of an action in a network where agents imitate successful past behavior of their neighbors. We find that the optimal targeting strategy depends on two parameters: (i) the likelihood of the action being more successful than its alternative and (ii) the planner's patience. More specifically, when the planner's preferred action has higher probability of being more successful than its alternative, then the optimal strategy for an infinitely patient planner is to concentrate all the targeted agents in one connected group; whereas when this probability is lower it is optimal to spread them uniformly around the network. Interestingly, for a very impatient planner, the optimal targeting strategy is exactly the opposite. Our results highlight the importance of knowing a society's exact network structure for the efficient design of targeting strategies, especially in settings where the agents are positionally similar.

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Paper provided by Job Market Papers in its series 2013 Papers with number pts99.

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Date of creation: 08 Dec 2013
Handle: RePEc:jmp:jm2013:pts99
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Find related papers by 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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  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.
  2. 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.
  3. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
  4. Schlag, Karl H., 1998. "Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits," Journal of Economic Theory, Elsevier, vol. 78(1), pages 130-156, January.
  5. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641.
  6. 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.
  7. József Sákovics & Jakub Steiner, 2012. "Who Matters in Coordination Problems?," American Economic Review, American Economic Association, vol. 102(7), pages 3439-3461, December.
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  12. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
  13. Tsakas Nikolas, 2014. "Imitating the Most Successful Neighbor in Social Networks," Review of Network Economics, De Gruyter, vol. 12(4), pages 1-33, February.
  14. Fernando Vega-Redondo, 1997. "The Evolution of Walrasian Behavior," Econometrica, Econometric Society, vol. 65(2), pages 375-384, March.
  15. Alós-Ferrer, Carlos & Weidenholzer, Simon, 2008. "Contagion and efficiency," Journal of Economic Theory, Elsevier, vol. 143(1), pages 251-274, November.
  16. Eshel, Ilan & Samuelson, Larry & Shaked, Avner, 1998. "Altruists, Egoists, and Hooligans in a Local Interaction Model," American Economic Review, American Economic Association, vol. 88(1), pages 157-179, March.
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Cited by:

  1. 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.

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