An adaptive agent bidding strategy based on stochastic modeling
Proceedings of the third annual conference on Autonomous Agents
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Multi-agent oriented constraint satisfaction
Artificial Intelligence
A genetic agent-based negotiation system
Computer Networks: The International Journal of Computer and Telecommunications Networking
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Price Formation in Double Auctions
E-Commerce Agents, Marketplace Solutions, Security Issues, and Supply and Demand
Agent-based computational economics: modeling economies as complex adaptive systems
Information Sciences—Informatics and Computer Science: An International Journal
An adaptive attitude bidding strategy for agents in continuous double auctions
Electronic Commerce Research and Applications
A long-term profit seeking strategy for continuous double auctions in a trading agent competition
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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The real-world continuous double auction CDA market is a dynamic environment. However, most of the existing agent bidding strategies are simply designed for static markets. A new detecting method for bidding strategy is necessary for more practical simulations and applications. In this paper, we present a novel agent-based computing approach called the GDX Plus GDXP model. In the proposed model, trades are decided according to the market events in history combined with the forecast of market trends. The GDXP model employs a dynamic adjustment mechanism to make the bidding strategy adapt to the shocks in a dynamic environment. The experimental results of the comparison between GDXP and other typical models, with respect to both static and dynamic CDA markets, demonstrate the performance of the GDXP model.