Adaptive Adjustment of Starting Price for Agents in Continuous Double Auctions
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Partially observable stochastic game-based multi-agent prediction markets
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A multi-agent prediction market based on partially observable stochastic game
Proceedings of the 13th International Conference on Electronic Commerce
A Generic Framework for a Combined Agent-based Market and Production Model
Computational Economics
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Online auctions are a platform to trade goods on the Internet. In this context, negotiation capabilities for software agents in continuous double auctions (CDAs) are a central concern. Agents need to be able to prepare bids for and evaluate offers on behalf of the users they represent with the aim of obtaining the maximum benefit for their users. For the agents, their bids are decided according to some bidding strategy. However, in CDAs, it is a complex decision problem because of the inherent uncertainty and dynamics of the auction market. In this book, we present a new bidding strategy for agents to adopt in CDAs and propose tools to enhance the performance of existing bidding strategies in CDAs. The superior performance of the new bidding strategy as well as the tools presented in this book are illustrated through extensive experiments.