Learning and Tacit Collusion by Artificial Agents in Cournot Duopoly Games

Steven O. Kimbrough (), Ming Lu () and Frederic Murphy ()
Additional contact information
Steven O. Kimbrough: University of Pennsylvania
Ming Lu: University of Pennsylvania
Frederic Murphy: Temple University

A chapter in Formal Modelling in Electronic Commerce, 2005, pp 477-492 from Springer

Abstract: Abstract We examine learning by artificial agents in repeated play of Cournot duopoly games. Our learning model is simple and cognitively realistic. The model departs from standard reinforcement learning models, as applied to agents in games, in that it credits the agent with a form of conceptual ascent, whereby the agent is able to learn from a consideration set of strategies spanning more than one period of play. The resulting behavior is markedly different from behavior predicted by classical economics for the single-shot (unrepeated) Cournot duopoly game. In repeated play under our learning regime, agents are able to arrive at a tacit form of collusion and set production levels near to those for a monopolist. We note that Cournot duopoly games are reasonable approximations for many real-world arrangements, including hourly spot markets for electricity.

Keywords: Reinforcement Learning; Electricity Market; Future Market; Repeated Game; Spot Market (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:ihichp:978-3-540-26989-2_19

Ordering information: This item can be ordered from
http://www.springer.com/9783540269892

DOI: 10.1007/3-540-26989-4_19

Access Statistics for this chapter

More chapters in International Handbooks on Information Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().