Strategic bidding in continuous double auctions

In this paper, we describe a novel bidding strategy that autonomous trading agents can use to participate in Continuous Double Auctions (CDAs). Our strategy is based on both short and long-term learning that allows such agents to adapt their bidding behaviour to be efficient in a wide variety of environments. For the shortterm learning, the agent updates the aggressiveness of its bidding behaviour (more aggressive means it will trade off profit to improve its chance of transacting, less aggressive that it targets more profitable transactions and is willing to trade off its chance of transacting to achieve them) based on market information observed after any bid or ask appears in the market. The long-term learning then determines how this aggressiveness factor influences an agent's choice of which bids or asks to submit in the market, and is based on market information observed after every transaction (successfully matched bid and ask). The principal motivation for the short-term ...

• Type: Journal
• Year: 2008
• Where: AI
• Authors: Perukrishnen Vytelingum, Dave Cliff, Nicholas R. Jennings
• Keywords: AI 2008, Artificial Intelligence, Continuous Double Auction, Long-term Learning, Market