Simulating sellers in online exchanges

Get BibTex-formatted data

Author

Subhajyoti Bandyopadhyay, Jackie Rees, John M. Barron

Tech report number

CERIAS TR 2005-118

Entry type

article

Abstract

Business-to-business (B2B) exchanges are expected to bring about lower prices for buyers through reverse auctions. Analysis of such settings for seller pricing behavior often points to mixed-strategy equilibria. In real life, it is plausible that managers learn this complex ideal behavior over time. We modeled the two-seller game in a synthetic environment, where two agents use a reinforcement learning (RL) algorithm to change their pricing strategy over time. We find that the agents do indeed converge towards the theoretical Nash equilibrium. The results are promising enough to consider the use of artificial learning mechanisms in electronic marketplace transactions.

Date

2005

Key alpha

B2B marketplaces; Reinforcement learning; Experimental economics; Game theory; Mixed-strategy equilibrium

Publisher

Elsevier B.V.

School

Purdue University and University of Florida

Publication Date

2005-01-01

Copyright

2004 Elsevier B.V.

Keywords

B2B marketplaces; Reinforcement learning; Experimental economics; Game theory; Mixed-strategy equilibrium

BibTex-formatted data

To refer to this entry, you may select and copy the text below and paste it into your BibTex document. Note that the text may not contain all macros that BibTex supports.

@Article{ B2B marketplaces; Reinforcement learning; Experimental economics; Game theory; Mixed-strategy equilibrium ,
	title = "Simulating sellers in online exchanges",
	author = "Subhajyoti Bandyopadhyay, Jackie Rees, John M. Barron",
	year = "2005",
	publisher = "Elsevier B.V.",
	school = "Purdue University and University of Florida",
	abstract = "Business-to-business (B2B) exchanges are expected to bring about lower prices for buyers through reverse auctions. 
Analysis of such settings for seller pricing behavior often points to mixed-strategy equilibria. In real life, it is plausible that 
managers learn this complex ideal behavior over time. We modeled the two-seller game in a synthetic environment, where two 
agents use a reinforcement learning (RL) algorithm to change their pricing strategy over time. We find that the agents do indeed 
converge towards the theoretical Nash equilibrium. The results are promising enough to consider the use of artificial learning 
mechanisms in electronic marketplace transactions. ",
	copyright = "2004 Elsevier B.V.",
	keywords = "B2B marketplaces; Reinforcement learning; Experimental economics; Game theory; Mixed-strategy equilibrium ",
}