Jiang Rong

AAMAS Conference 2019 Conference Paper

Competitive Bridge Bidding with Deep Neural Networks

• Jiang Rong
• Tao Qin
• Bo An

The game of bridge consists of two stages: bidding and playing. While playing is proved to be relatively easy for computer programs, bidding is very challenging. During the bidding stage, each player knowing only his/her own cards needs to exchange information with his/her partner and interfere with opponents at the same time. Existing methods for solving perfect-information games cannot be directly applied to bidding. Most bridge programs are based on human-designed rules, which, however, cannot cover all situations and are usually ambiguous and even conflicting with each other. In this paper, we, for the first time, propose a competitive bidding system based on deep learning techniques, which exhibits two novelties. First, we design a compact representation to encode the private and public information available to a player for bidding. Second, based on the analysis of the impact of other players’ unknown cards on one’s final rewards, we design two neural networks to deal with imperfect information, the first one inferring the cards of the partner and the second one taking the outputs of the first one as part of its input to select a bid. Experimental results show that our bidding system outperforms the top rule-based program.

AAAI Conference 2018 Conference Paper

Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand

• Jiang Rong
• Tao Qin
• Bo An

The market for selling reusable products (e. g. , car rental, cloud services and network access resources) is growing rapidly over the last few years, where service providers maximize their revenues through setting optimal prices. While there has been lots of research on pricing optimization, existing works often ignore dynamic property of demand and the competition among providers. Thus, existing pricing solutions might be far from optimal in realistic markets. This paper provides the first study of service providers’ dynamic pricing in consideration of market competition and makes three key contributions along this line. First, we propose a comprehensive model that takes into account the dynamic demand and interaction among providers, and formulate the optimal pricing policy in the competitive market as an equilibrium. Second, we propose an approximate Nash equilibrium to describe providers’ behaviors, and design an efficient algorithm to compute the equilibrium which is guaranteed to converge. Third, we derive many properties of the model without any further constraints on demand functions, which can reduce the search space of policies in the algorithm. Finally, we conduct extensive experiments with different parameter settings, showing that the approximate equilibrium is very close to the Nash equilibrium and our proposed pricing policy outperforms existing strategies.

AAMAS Conference 2017 Conference Paper

Pricing Optimization for Selling Reusable Resources

• Jiang Rong
• Tao Qin
• Bo An
• Tie-Yan Liu

AAAI Conference 2017 Conference Paper

Revenue Maximization for Finitely Repeated Ad Auctions

• Jiang Rong
• Tao Qin
• Bo An
• Tie-Yan Liu

Reserve price is an effective tool for revenue maximization in ad auctions. The optimal reserve price depends on bidders’ value distributions, which, however, are generally unknown to auctioneers. A common practice for auctioneers is to first collect information about the value distributions by a sampling procedure and then apply the reserve price estimated with the sampled bids to the following auctions. In order to maximize the total revenue over finite auctions, it is important for the auctioneer to find a proper sample size to trade off between the cost of the sampling procedure and the optimality of the estimated reserve price. We investigate the sample size optimization problem for Generalized Second Price auctions, which is the most widely-used mechanism in ad auctions, and make three main contributions along this line. First, we bound the revenue losses in the form of competitive ratio during and after sampling. Second, we formulate the problem of finding the optimal sample size as a non-convex mixed integer optimization problem. Then we characterize the properties of the problem and prove the uniqueness of the optimal sample size. Third, we relax the integer optimization problem to a continuous form and develop an efficient algorithm based on the properties to solve it. Experimental results show that our approach can significantly improve the revenue for the auctioneer in finitely repeated ad auctions.

ECAI Conference 2016 Conference Paper

Modeling Bounded Rationality for Sponsored Search Auctions

• Jiang Rong
• Tao Qin 0001
• Bo An 0001
• Tie-Yan Liu

Sponsored search auctions (SSAs) have attracted a lot of research attention in recent years and different equilibrium concepts have been studied in order to understand advertisers' bidding strategies. However, the assumption that advertisers are perfectly rational in these studies is unrealistic in the real world. In this work, we apply the quantal response equilibrium (QRE), which is powerful in modeling bounded rationality, to SSAs. Due to high computational complexity, existing methods for QRE computation have very poor scalability for SSAs. Through exploiting the structures of QRE for SSAs, this paper presents an efficient homotopy-based algorithm to compute the QRE for large-size SSAs, which features the following two novelties: 1) we represent the SSAs as an Action Graph Game (AGG) which can compute the expected utilities in polynomial time; 2) we further significantly reduce redundant calculations by leveraging the underlying relations between advertisers' utilities. We also develop an estimator to infer parameters of SSAs and fit the QRE model into a dataset from a commercial search engine. Our experimental results indicate that the algorithm can significantly improve the scalability of QRE computation for SSAs and the QRE model can describe the real-world bidding behaviors in a very accurate manner.

AAMAS Conference 2016 Conference Paper

Optimal Sample Size for Adword Auctions (Extended Abstract)

• Jiang Rong
• Tao Qin
• Bo An
• Tie-Yan Liu

Generalized Second Price (GSP) mechanism is widely used in ad auctions and reserve price is an effective tool for revenue maximization. The optimal reserve price depends on bidders’ value distribution, which, however, is generally unknown to auctioneers. A common practice for auctioneers is to first collect information about the value distribution by a sampling procedure and then apply the reserve price estimated with the sampled bids to the following auctions. In order to maximize his/her total revenue over finite GSP ad auctions, it is important for the auctioneer to find a proper sample size to trade off between the cost of the sampling procedure and the optimality of the estimated reserve price. We first propose the revenue bounds during and after sampling. Then we formulate the problem of finding the optimal sample size that maximizes the auctioneer’s worse-case total revenue as an constrained optimization problem, the solution of which is independent of the value distribution.