Abstract
We study a standard model of economic agents on the nodes of a social network graph who learn a binary "state of the world" S, from initial signals, by repeatedly observing each other's best guesses. Asymptotic learning is said to occur on a family of graphs Gn = (Vn,En) with {pipe}Vn{pipe} → ∞ if with probability tending to 1 as n → ∞ all agents in Gn eventually estimate S correctly. We identify sufficient conditions for asymptotic learning and contruct examples where learning does not occur when the conditions do not hold.
Original language | English (US) |
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Pages (from-to) | 127-157 |
Number of pages | 31 |
Journal | Probability Theory and Related Fields |
Volume | 158 |
Issue number | 1-2 |
DOIs | |
State | Published - Feb 2014 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Analysis
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- Aggregation of information
- Bayesian learning
- Rational expectations
- Social networks