A monopolist offers a product to a market of consumers with heterogeneous quality preferences. Although initially uninformed about the product quality, they learn by observing past purchase decisions and reviews of other consumers. Our goal is to analyze the social learning mechanism and its effect on the seller's pricing decision. This analysis borrows from the literature on social learning and on pricing and revenue management.Consumers follow a naive decision rule and, under some conditions, eventually learn the product's quality. Using mean-field approximation, the dynamics of this learning process are characterized for markets with high demand intensity. The relationship between the price and the speed of learning depends on the heterogeneity of quality preferences. Two pricing strategies are studied: a static price and a single price change. Properties of the optimal prices are derived. Numerical experiments suggest that pricing strategies that account for social learning may increase revenues considerably relative to strategies that do not.
We consider dynamic oligopoly models in the spirit of Ericson and Pakes (1995). We introduce a new computationally tractable model for industries with a few dominant firms and many fringe firms. This is a prevalent market structure in consumer and industrial goods. In our model, firms keep track of the detailed state of dominant firms and of few moments of the distribution that describes the states of fringe firms. Based on this idea we introduce a new equilibrium concept that we call moment-based Markov equilibrium (MME). MME is behaviorally appealing and computationally tractable. However, MME can suffer from an important pitfall. Because moments may not summarize all payoff relevant information, MME strategies may not be optimal. We propose different approaches to overcome this difficulty with varying degrees of restrictions on the model primitives and strategies. Our first approach introduces models for which moments summarize all payoff relevant history and therefore for which MME strategies are optimal. The second approach restrict fringe firm strategies so that again moments become sufficient statistics. The third approach does not impose such restrictions, but introduces a computational error bound to asses the degree of sub-optimality of MME strategies. This bound allows to evaluate whether a finer state aggregation is necessary, for example by adding more moments. We provide computational experiments to show that our algorithms and error bound work well in practice for important classes of models. We also show that, cumulatively, fringe firms discipline dominant firms to behave more competitively, and that ignoring fringe firms in counterfactual analysis may lead to incorrect conclusions. Our model significantly extends the class of dynamic oligopoly models that can be studied computationally. In addition, our methods can also be used to improve approximations in other contexts such as dynamic industry models with an infinite number of heterogeneous firms and an aggregate shock; stochastic growth models; and dynamic models with forward-looking consumers.
A monopolist offers a product to a market of consumers with heterogeneous quality preferences. Although initially uninformed about the product quality, they learn by observing past purchase decisions and reviews of other consumers. Our goal is to analyze the social learning mechanism and its effect on the seller's pricing decision. This analysis borrows from the literature on social learning and on pricing and revenue management.Consumers follow a naive decision rule and, under some conditions, eventually learn the product's quality. Using mean-field approximation, the dynamics of this learning process are characterized for markets with high demand intensity. The relationship between the price and the speed of learning depends on the heterogeneity of quality preferences. Two pricing strategies are studied: a static price and a single price change. Properties of the optimal prices are derived. Numerical experiments suggest that pricing strategies that account for social learning may increase revenues considerably relative to strategies that do not.
When buying a new product online, potential consumers often read online reviews to get an understanding of the quality of the product, and then they decide whether to make a purchase. In turn, people who purchase the product submit reviews to the aggregator site, thus adding new information over time. This paper is the first to study such a social learning mechanism based on online reviews under the assumption that consumers are Bayesian. It identifies assumptions under which consumers eventually learn the true quality and explores the optimal pricing policy for a seller who participates in such a market.
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