Threshold learning dynamics in social networks

TL;DR

This paper investigates how threshold-based social learning dynamics influence the accuracy of information aggregation in social networks, revealing that only certain threshold regimes lead to correct collective learning.

Contribution

It challenges existing models by showing that simple threshold processes do not always result in effective social learning, identifying three regimes and highlighting the role of limited interaction.

Findings

Correct social learning occurs only in an intermediate threshold regime.

Limited interaction broadens the conditions for successful learning.

Extreme thresholds lead to system freezing or persistent flux.

Abstract

Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take respect to an important issues, typically confront external signals to the information gathered from their contacts. Received economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs.