Dynamic pricing strategies under a finite time horizon

ABSTRACT

In the near future, dynamic pricing will be a common competitive maneuver. In this age of digital markets, sellers in electronic marketplaces can implement automated and frequent adjustments to prices and can easily imagine how this will increase their revenue by selling to buyers "at the right time, at the right price." But at present, most sellers do not have an adequate understanding of the performance of dynamic pricing algorithms in their marketplaces. This paper addresses this concern by analyzing the performance of two adaptive pricing algorithms. We study the behavior of these algorithms within the Learning Curve Simulator, a platform for analyzing dynamic pricing strategies in finite markets assuming various buyer behaviors. The goals of our research are twofold: (i) to explore the use of simulation as a tool to aid in the development of dynamic pricing strategies; and (ii) to explicitly identify the market conditions under which our example strategies, Goal-Directed and Derivative-Following, are successful.

REFERENCES

• 1: Boyd, E.A. "Airline Alliance Revenue Management." ORMS Today. (October 1998).
• 2: Cary A. Deck , Bart J. Wilson, Interactions of automated pricing algorithms: an experimental investigation, Proceedings of the 2nd ACM conference on Electronic commerce, p.77-85, October 17-20, 2000, Minneapolis, Minnesota, United States [doi>10.1145/352871.352880]
• 3: Gallego, G. and van Ryzin, G. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management." Operations Research. vol. 45 (1). 24-41.
• 4: Guillermo Gallego , Garrett van Ryzin, Optimal dynamic pricing of inventories with stochastic demand over finite horizons, Management Science, v.40 n.8, p.999-1020, Aug. 1994 [doi>10.1287/mnsc.40.8.999]
• 5: Jeffrey O. Kephart , James E. Hanson , Amy R. Greenwald, Dynamic pricing by software agents, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.32 n.6, p.731-752, May 30 2000 [doi>10.1016/S1389-1286(00)00026-8]
• 6: Pinar Keskinocak , Shidhar Tayur, Quantitative Analysis for Internet-Enabled Supply Chains, Interfaces, v.31 n.2, p.70-89, March 2001 [doi>10.1287/inte.31.2.70.10626]
• 7: Morris, J. "A Simulation-based Approach to Dynamic Pricing." Master's Thesis in Media Arts & Sciences, MIT, Cambridge, May 2001.
• 8: Morris, J. and Maes, P. "Understanding Dynamic Pricing Agents." Software Demos, Fourth International Conference on Autonomous Agents (Agents 2001), Montreal, Canada (June 2001).
• 9: Morris, J., Maes, P. and Greenwald, A. "Learning Curve: Analysis of an Agent Pricing Strategy Under Varying Conditions." Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI'2001), pp. 1135- 1141, Las Vegas, NV (June 2001).
• 10: Barry C. Smith , Dirk P. Günther , B. Venkateshwara Rao , Richard M. Ratlife, E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution, Interfaces, v.31 n.2, p.37-55, March 2001 [doi>10.1287/inte.31.2.37.10627]
• 11: Smith, M., Bailey, J. and Brynjolfsson, E. "Understanding Digital Markets: Review and Assessment." in Brynjolfsson, E. and Kahin, B. eds. Understanding the Digital Economy, MIT Press, Cambridge, MA, 2000.

INDEX TERMS

General Terms:
Algorithms, Economics, Experimentation, Measurement

Keywords:
agent simulation, buyer behavior, dynamic pricing, electronic markets, pricing strategies