Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.dss.2004.08.015 | Decision Support Systems |
Keywords | Field | DocType |
reinforcement learning,real life,game theory,lower price,reverse auction,experimental economics,seller pricing behavior,complex ideal behavior,online exchange,b2b marketplaces,electronic marketplace transaction,simulating seller,mixed-strategy equilibrium,synthetic environment,pricing strategy,artificial learning mechanism,mixed strategy,nash equilibrium | Transaction processing,Experimental economics,Mathematical economics,Strategy,Computer science,Game theory,Nash equilibrium,Reverse auction,Business-to-business,Reinforcement learning | Journal |
Volume | Issue | ISSN |
41 | 2 | Decision Support Systems |
Citations | PageRank | References |
8 | 1.08 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhajyoti Bandyopadhyay | 1 | 524 | 35.12 |
| Jackie Rees | 2 | 182 | 11.74 |
| John M. Barron | 3 | 47 | 6.36 |