Multi-Dimensional Social Learning

45 Pages Posted: 14 Jun 2013 Last revised: 10 May 2018

See all articles by Itai Arieli

Itai Arieli

Technion-Israel Institute of Technology

Manuel Mueller-Frank

University of Navarra, IESE Business School

Date Written: April 25, 2018


This paper provides a model of social learning where the order in which actions are taken is determined by an $m$-dimensional integer lattice rather than along a line as in the herding model. The observation structure is determined by a random network. Every agent links to each of his preceding lattice neighbors independently with probability p, and observes the actions of all agents that are reachable via a directed path in the realized social network. For m\geq 2, we show that as p<1 goes to one, (i) so does the asymptotic proportion of agents who take the optimal action, (ii) this holds for any informative signal distribution, and (iii) bounded signal distributions might achieve higher expected welfare than unbounded signal distributions. By contrast, if signals are bounded and p=1, all agents select the suboptimal action with positive probability.

Keywords: Social Learning, Lattice, informational cascades

Suggested Citation

Arieli, Itai and Mueller-Frank, Manuel, Multi-Dimensional Social Learning (April 25, 2018). Available at SSRN: or

Itai Arieli

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000

Manuel Mueller-Frank (Contact Author)

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034

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