The Impact of Communication Costs and Limitations on Price Wars in an Information Economy
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Simulating the Behaviour of Electronic MarketPlaces with an Agent-Based Approach
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
Agent-based simulation of electronic marketplaces with decision support
Proceedings of the 2008 ACM symposium on Applied computing
A multi-agent system for the support of producer coalition formation in electricity markets
Proceedings of the 2008 ACM symposium on Applied computing
Effectiveness of Q-learning as a tool for calibrating agent-based supply network models
Enterprise Information Systems
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
Expert Systems with Applications: An International Journal
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By employing dynamic pricing, sellers have the potential to increase their revenue by selling their goods at prices customized to the buyers' demand, the market environment, and the seller's supply at the moment of the transaction. As dynamic pricing becomes a necessary competitive maneuver, and as market mechanisms become more complex, there is a growing need for software agents to be used to automate the task of implementing instantaneous price changes. But prior to using dynamic pricing agents, sellers need to understand the implications of agent pricing strategies on their marketplaces. The following article presents the Learning Curve Simulator, a market simulator designed for analyzing agent pricing strategies in markets under finite time horizons and fluctuation buyer demand. Through an in-depth description of the simulator's capabilities and an example of strategy analysis, we demonstrate the strength of a simulation-based approach to understanding agent pricing strategies.