The benefit of sequentiality in social networks *

Abstract This paper examines the benefit of sequentiality in the social networks. We adopt the elegant theoretical framework proposed by We then examine the structure of optimal mechanism and allow for arbitrary sequence of players' moves. We show that starting from any fixed sequence, the aggregate contribution always goes up while making simultaneous-moving players move sequentially. This suggests a robust rule of thumbs -any local modification towards the sequential-move game is beneficial. Pushing this idea to the extreme, the optimal sequence turns out to be a chain structure, i.e., players should move one by one. Our results continue to hold when either players exhibit strategic substitutes instead or the network designer's goal is to maximize the players' aggregate payoff rather than the aggregate contribution

Dynamic Markets with Many Agents: Applications in Social Learning and Competition

This thesis considers two applications in dynamics economic models with many agents. The dynamics of the economic systems under consideration are intractable since they depend on the (stochastic) outcomes of the agents' actions. However, as the number of agents grows large, approximations to the aggregate behavior of agents come to light. I use this observation to characterize market dynamics and subsequently to study these applications. Chapter 2 studies the problem of devising a pricing strategy to maximize the revenues extracted from a stream of consumers with heterogenous preferences. Consumers, however, do not know the quality of the product or service and engage in a social learning process to learn it. Using a mean-field approximation the transient of this social learning process is uncovered and the pricing problem is analyzed. Chapter 3 adds to the previous chapter in analyzing features of this social learning process with finitely many agents. In addition, the chapter generalizes the information structure to include cases where consumers take into account the order in which reviews were submitted. Chapter 4 considers a model of dynamic oligopoly competition in the spirit of models that are widespread in industrial organization. The computation of equilibrium strategies of such models suffers from the curse of dimensionality when the number of agents (firms) is large. For a market structure with few dominant firms and many fringe firms, I study an alternative equilibrium concept in which fringe firms are represented succinctly with a low dimensional set of statistics. The chapter explores how this new equilibrium concept expands the class of dynamic oligopoly models that can be studied computationally in empirical work

Bayesian social learning with consumer reviews

We study a market of heterogeneous customers who rationally learn the mean quality of an offered product by observing the reviews of customers who purchased the product earlier in time. The seller, who is equally uniformed about the quality, prices dynamically to maximize her revenue. We find that social learning is successful|agents eventually learning the mean quality of the product. This result holds for an information structure when the sequence of past re- views and prices is observed, and, under some assumptions, even when only aggregate reviews are observed. The latter result hinges on the observation that earlier reviews are more inuential than later one. In addition, we find that under general conditions the seller benefits from social learning ex ante|before knowing the quality of her product. Finally, we draw conclusions on the sellers pricing problem when accounting for social learning. Under some assumptions, we find that lowering the price speeds social learning, in contrast with earlier results on social learning from privately observed signals

Monopoly Pricing in the Presence of Social Learning

To be submitted on November 2011 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

Monopoly Pricing in the Presence of Social Learning

4sìreservedA 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. Consumers follow an intuitive, non-Bayesian decision rule. Under conditions that we identify, we show that consumers eventually learn the product’s quality. We show how the learning trajectory can be approximated in settings with high demand intensity via a mean-field approximation that highlights the dynamics of this learning process, its dependence on the price, and the market heterogeneity with respect to quality preferences. Two pricing policies are studied: a static price and one with a single price change. Finally, numerical experiments suggest that pricing policies that account for social learning may increase revenues considerably relative to policies that do not.mixedCrapis, Davide; Ifrach, Bar; Maglaras, Costis; Scarsini, MarcoCrapis, Davide; Ifrach, Bar; Maglaras, Costis; Scarsini, Marc