Editorial Statement—Market Design, Platform, and Demand Analytics
MARKET DESIGN, PLATFORM, AND DEMAND ANALYTICS
Itai Ashlagi, Stanford University
Martin Bichler, Technical University of Munich
Srikanth Jagabathula, New York University
Many societal and business challenges find solutions in the careful design of interactions among market participants and, more broadly, processes that seek to efficiently allocate scarce resources. Academic research in the field seeks to shape our understanding of the interactions among the agents and of the possible outcomes for the various participants.
Recent decades have seen rapid technological progress in information, communication, and computation, and these have transformed how economic agents, such as sellers, platforms, and buyers, interact with each other. The rise of digital interactions has greatly increased our ability to collect detailed data on customer and firm interactions and exert unprecedented levels of control over the design, implementation, and operation of the markets. More than ever before, we are able to engineer various aspects of market transactions, such as revenue management, real-time pricing analytics (e.g., as in ride-sharing), personalized promotions, personalized assortments, search, bundling/unbundling, subscriptions, recommendations, rating and review systems, digital advertising, capacity, liquidity, information, terms of trade, and transaction fees. Such questions fall under the general umbrella of the design, operations, and management of marketplaces.
We invite contributions that explore a wide array of issues related to the theory and practice of demand management, pricing, and the design of markets and platforms whether at tactical or strategic levels and from the perspectives of both market operators and participants. Our call extends to studies on all market types whether emerging or established.
The market design, platform, and demand analytics department seeks well-written papers that are grounded around important applications and have the potential to impact practice. The types of contributions we seek are broad, ranging from improving the understanding of the application domain at hand and opening up new relevant problem areas to devising novel algorithms or uncovering new insights. The department is open to all approaches, including modeling, theoretical, empirical, field experiments, and computational approaches. Many papers will leverage tools from optimization, game theory, econometrics, behavioral modeling, machine learning, online learning, and reinforcement learning, among others. We also welcome papers that deal with issues of algorithmic fairness, bias, platform governance, overall welfare, decentralized platforms, and artificial intelligence–driven technologies. Studies that demonstrate how to solve a relevant practical problem, ideally with real-world data or in collaboration with a practitioner, are welcome.
