Adaptive Adjustment of Starting Price for Agents in Continuous Double Auctions
Software agents can act flexibly in a variety of electronic marketplaces. Continuous Double Auction (CDA) is an efficient and common form of these marketplaces. There are several bidding strategies proposed in the literature for agents to adopt to compute their asks or bids in CDAs. For all of these bidding strategies, starting price has not been taken into account. However, in online auction marketplaces, the starting price is an important parameter for sellers to set and has been discussed many a time in the literature. Given the importance of starting price, the main objective of our work is to explore the effect of starting price on agents using various bidding strategies and how to adjust it adaptively within a dynamic CDA market. Experimental results confirm that when agents set their starting prices at varying values in different market situations, their profit changes significantly no matter which strategy they adopt. In order to guide agents to adjust their starting prices in dynamic and unknown markets, an adaptive mechanism is proposed. Experimental results show that agents adopting the adaptive mechanism generally outperform the corresponding agents without. Furthermore, another set of experiments are carried out to let all the agents use the adaptive mechanism and compete together in one market. Not surprisingly, the profit of agents is observed to drop down a lot in this situation.
KeywordsAdaptive Mechanism Reservation Price Belief Function Bidding Strategy Transaction Price
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