ICES: Small: Collaborative Research: Selling to Networked Markets

New York University, New York NY

Investigators

Abstract

Modern marketplaces, enabled by recent advances in information technology, are becoming ever more complex, dynamic and interconnected. Customers are increasingly more informed about the choices available to them and are thus able to make better decisions. For firms, this presents both opportunities and challenges. On the positive side, the firm has a bigger than ever array of tools at its disposal that enable it to better sell to its customers. Such tools are both computational (pricing algorithms, customer targeting data, social networking information) and economical (dynamic mechanisms) in nature. The challenging aspect for the firm is that modern technology also allows consumers to easily learn the opinions of fellow customers and to be strategic in their decision-making. Having such strategic and networked customers is not necessarily a net negative for the firm, but it certainly makes the firm's problem of how to sell its products a more complex one. This project aims to develop a framework for understanding how to sell goods in markets that are fundamentally dynamic and interconnected. The project will research what mechanisms are revenue-optimal (or near-optimal, while being simple and computationally tractable) in dynamic settings where customers can learn from each other. The PIs will study the effect of network learning on the firm's behavior and whether it forces the firm to share some of the costs associated with consumer learning. The project will also focus on how the social networkstructure affects the optimal mechanism and will try to understand whether such network learning effects lead to lower (or higher) revenue for the firm and aggregate consumer welfare. Fundamental research in the dynamics of networked markets provides deeper knowledge to a broad audience that includes firms, consumer groups and regulators. Curriculum development at the interface of operations research, information systems and computer science will benefit from this research experience.