Learning competitive pricing strategies by multi-agent reinforcement learning

Erich Kutschinski, Thomas Uthmann and Daniel Polani

Journal of Economic Dynamics and Control, 2003, vol. 27, issue 11, 2207-2218

Abstract: In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is expensive to train.

Keywords: Distributed simulation; Agent-based computational economics; Dynamic pricing; Multi-agent reinforcement learning; Q-learning (search for similar items in EconPapers)
JEL-codes: C63 (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165188902001227
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:27:y:2003:i:11:p:2207-2218

DOI: 10.1016/S0165-1889(02)00122-7

Access Statistics for this article

Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok

More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Catherine Liu ().