Paper Info
Title
Reinforcement Mechanism Design: With Applications To Dynamic Pricing In Sponsored Search Auctions
Abstract
In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.
Year
Venue
Field
2017
arXiv: Computer Science and Game Theory
Mathematical optimization,Economics,Search engine,Dynamic pricing,Markov decision process,Common knowledge,Common value auction,Mechanism design,Bidding,Reinforcement learning
DocType
Volume
ISSN
Journal
34
2159-5399
Citations 
PageRank 
References 
2
0.37
1
Authors
10
Name
Order
Citations
PageRank
Weiran Shen158.25
Binghui Peng264.14
Hanpeng Liu320.37
Michael G. Zhang442.10
Ruohan Qian520.37
Yan Hong620.37
Zhi Guo752.08
Zongyao Ding861.82
Pengjun Lu920.37
Pingzhong Tang1013332.06