Simulating sellers in online exchanges

doi:10.1016/j.dss.2004.08.015

2006-01-01

PRE

Subhajyoti Bandyopadhyay

Jackie Rees

John M. Barron

DOI：10.1016/j.dss.2004.08.015

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摘要

En 中文

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. (c) 2004 Elsevier B.V. All rights reserved.

Keyword:

B2B marketplaces

reinforcement learning

experimental economics

game theory

mixed-strategy equilibrium

期刊

Decision Support Systems

IF:

6.8

论文数:

3.8K

被引数:

1.5W

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