Social Learning in Non-Stationary Environments
This addresses decision-making under uncertainty for consumers in markets with changing product qualities, though it is incremental as it extends known static results.
The paper tackles the problem of Bayesian consumers learning product quality from multi-dimensional reviews in non-stationary environments, showing that beliefs converge to true quality with provided rates and small learning costs in dynamic settings.
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.