Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand

Authors

  • Jiang Rong Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences
  • Tao Qin Microsoft Research
  • Bo An Nanyang Technological University

Keywords:

cloud computing, dynamic pricing, poisson process

Abstract

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.

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Published

2018-04-26

How to Cite

Rong, J., Qin, T., & An, B. (2018). Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11591

Issue

Section

AAAI Technical Track: Multiagent Systems