Abstract
In this paper, we consider the problem of dynamic pricing by a set of competing sellers in an information economy where buyers differentiate products along multiple attributes, and buyer preferences can change temporally. Previous research in this area has either focused on dynamic pricing along a limited number of (e.g. binary) attributes, or, assumes that each seller has access to private information such as preference distribution of buyers, and profit/price information of other sellers. However, in real information markets, private information about buyers and sellers cannot be assumed to be available a priori. Moreover, due to the competition between sellers, each seller faces a tradeoff between accuracy and rapidity of the pricing mechanism. In this paper, we describe a multi-attribute dynamic pricing algorithm based on minimax regret that can be used by a seller’s agent called a pricebot, to maximize the seller’s utility. Our simulation results show that the minimax regret based dynamic pricing algorithm performs significantly better than other algorithms for rapidly and dynamically tracking consumer attributes without using any private information from either buyers or sellers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bizrate Inc., http://www.bizrate.com
MySimon, http://www.mysimon.com
PriceGrabber, http://www.pricegrabber.com
Balcan, M.-F., Blum, A.: Approximation algorithms and online mechanisms for item pricing. In: Proc. ACM-EC, pp. 29–35 (2006)
Bergemann, D., Ozmen, D.: Optimal pricing with recommender systems. In: Proc. ACM-EC, pp. 43–51 (2006)
Boutilier, C., Patrascu, R., Poupart, P., Schuurmans, D.: Constraint-based optimization and utility elicitation using the minimax decision criterion. Artificial Intelligence 170(8), 686–713 (2006)
Chajewska, U., Koller, D., Parr, R.: Making rational decisions using adaptive utility elicitation. In: Proc. of the 17th National Conference on AI, pp. 363–369 (2006)
Conitzer, V.: Eliciting single-peaked preferences using comparison queries. In: Proc. AAMAS, pp. 408–415 (2007)
Dasgupta, P., Hashimoto, Y.: Multi-attribute Dynamic Pricing for Online Markets using Intelligent Agents. In: Proc. AAMAS, pp. 277–284 (2004)
DiMicco, J.M., Greenwald, A., Maes, P.: Dynamic Pricing Strategies under a Finite Time Horizon. In: Proc. ACM-EC, pp. 95–104 (2001)
Greenwald, A., Kephart, J.: Probabilistic Pricebots. In: Proc. of the 5th International Conference on Autonomous Agents, pp. 560–567 (2001)
Guruswami, V., Hartline, J., Karlin, A., Kempe, D., Kenyon, C.: On profit-maximizing envy-free pricing. In: Proc. 16th annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1164–1173 (2005)
Keeney, R., Raiffa, H.: Decisions with Multiple Objectives. Cambridge University Press, Cambridge (1993)
Kephart, J., Hanson, J., Greenwald, A.: Dynamic pricing by software agents. Computer Networks 32(6), 731–752 (2000)
Lahaie, S., Parkes, D.: Applying learning algorithms to preference elicitation. In: Proc. of the ACM Conference on Electronic Commerce, pp. 180–188 (2004)
Li, N., Zhang, P.: Consumer Online Shopping Attitudes and Behavior: An Assessment of Research. In: 8th Americas Conference on Information Systems (2002)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proc. ACM Conference on Human Factors in Computing Systems, pp. 210–217 (1995)
Wei, Y.: Future Orientation, Chronological Age and Product Attributes Preference. PhD Thesis, Georgia State University (2007)
Wang, T., Boutilier, C.: Incremental utility elicitation with the minimax regret decision criterion. In: Proc. IJCAI 2003, pp. 309–316 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jumadinova, J., Dasgupta, P. (2010). Multi-attribute Regret-Based Dynamic Pricing. In: Ketter, W., La Poutré, H., Sadeh, N., Shehory, O., Walsh, W. (eds) Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis. AMEC TADA 2008 2008. Lecture Notes in Business Information Processing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15237-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-15237-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15236-8
Online ISBN: 978-3-642-15237-5
eBook Packages: Computer ScienceComputer Science (R0)