One paper from Gaoling School of Artificial Intelligence, Renmin University of China (GSAI) was accepted by the 2021 ACM Conference on Economics and Computation (EC), according to the recently released schedule. EC focuses on the interdisciplinary study of economics and computer science. It is one of the top international conferences in the field, with an acceptance rate of about 26%.

Since January 2021, GSAI has published (including those being accepted) 49 papers in CCF A-category international journals or conferences, 8 papers in CCF B-category journals and conferences. Among them, 52 papers have GSAI students or faculties listed as their first or corresponding authors.

The recently accepted paper by SHEN Weiran, Tenure-track Assistant Professor of GSAI, was one of the results of “AI + Economics", an interdisciplinary research project of GSAI.

Since the founding of the Intelligent Social Governance Center and Artificial Intelligence Interdisciplinary Collaboration Platform of Renmin University of China, GSAI has been committed to building a one-stop new-type interdisciplinary research platform for the entire university, with a view to developing AI-based interdisciplinary research methods with the characteristics of Renmin University of China, to explore new model to empower high-level "AI + X"  talents, and to make original innovations and breakthroughs in frontiers of common concern and with high social relevance.

Paper introduction

Paper Title: Optimal Pricing of Information

Authors: Shuze Liu, Weiran Shen, Haifeng Xu

Paper Overview:

A decision maker looks to take an active action (e.g., purchase some goods or make an investment). The payoff of this active action depends on his own private type as well as a random and unknown state of nature. To decide between this active action and another passive action, which always leads to a safe constant utility, the decision maker may purchase information from an information seller. The seller can access the realized state of nature, and this information is useful for the decision maker (i.e., the information buyer) to better estimate his payoff from the active action.

We study the seller's problem of designing a revenue-optimal pricing scheme to sell her information to the buyer. Suppose the buyer's private type and the state of nature are drawn from two independent distributions, we fully characterize the optimal pricing mechanism for the seller in closed form. Specifically, under a natural linearity assumption of the buyer payoff function, we show that an optimal pricing mechanism is the threshold mechanism which charges each buyer type some upfront payment and then reveals whether the realized state is above some threshold or below it. The payment and the threshold are generally different for different buyer types, and are carefully tailored to accommodate the different amount of risks each buyer type can take. The proof of our results relies on novel techniques and concepts, such as upper/lower virtual values and their mixtures, which may be of independent interest.