Social Learning in Non-Stationary Environments

TL;DR

This paper studies how Bayesian consumers learn about multi-dimensional, possibly changing product quality through reviews, extending static models to dynamic, non-stationary environments with convergence analysis and low learning costs.

Contribution

It introduces a dynamic model for multi-dimensional, non-stationary quality, providing convergence rates and demonstrating low learning costs in changing environments.

Findings

Beliefs converge to true quality in static settings.

Convergence rates are established for dynamic environments.

Learning costs remain small despite quality fluctuations.

Abstract

Potential buyers of a product or service, before making their decisions, tend to read reviews written by previous consumers. We consider Bayesian consumers with heterogeneous preferences, who sequentially decide whether to buy an item of unknown quality, based on previous buyers' reviews. The quality is multi-dimensional and may occasionally vary over time; the reviews are also multi-dimensional. In the simple uni-dimensional and static setting, beliefs about the quality are known to converge to its true value. Our paper extends this result in several ways. First, a multi-dimensional quality is considered, second, rates of convergence are provided, third, a dynamical Markovian model with varying quality is studied. In this dynamical setting the cost of learning is shown to be small.