CSE Researchers Employ Game Theory and Machine Learning to Develop Better Bidding Strategies   Bookmark and Share


Professor Michael Wellman and doctoral student Julian Schvartzman have conducted comprehensive research into continuous double auction bidding strategy, and will soon share their findings. A continuous double auction is an ever-changing market in which bidders exchange offers to both buy and sell, and transactions occur as soon as participants agree on a price.

The researchers have applied game theory and reinforcement learning in new ways to develop best bidding strategies for these dynamic environments. They will present their findings on May 15 at the International Joint Conference on Autonomous Agents and Multiagent Systems in Budapest, Hungary.

For more information, see:

U-M News Service press release, May 13, 2009