Dynamic pricing by software agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
Interactions of automated pricing algorithms: an experimental investigation
Proceedings of the 2nd ACM conference on Electronic commerce
Intermediaries in an electronic trade network [Extended Abstract]
Proceedings of the 4th ACM conference on Electronic commerce
Efficient agents for cliff-edge environments with a large set of decision options
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Enhancing digital advertising using dynamically configurable multimedia
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Modeling viral economies for digital media
Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008
Learning approaches for developing successful seller strategies in dynamic supply chain management
Information Sciences: an International Journal
Adaptive Strategies for Dynamic Pricing Agents
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A dynamic pricing approach in e-commerce based on multiple purchase attributes
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Efficient bidding strategies for Cliff-Edge problems
Autonomous Agents and Multi-Agent Systems
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In the near future, dynamic pricing will be a common competitive maneuver. In this age of digital markets, sellers in electronic marketplaces can implement automated and frequent adjustments to prices and can easily imagine how this will increase their revenue by selling to buyers "at the right time, at the right price." But at present, most sellers do not have an adequate understanding of the performance of dynamic pricing algorithms in their marketplaces. This paper addresses this concern by analyzing the performance of two adaptive pricing algorithms. We study the behavior of these algorithms within the Learning Curve Simulator, a platform for analyzing dynamic pricing strategies in finite markets assuming various buyer behaviors. The goals of our research are twofold: (i) to explore the use of simulation as a tool to aid in the development of dynamic pricing strategies; and (ii) to explicitly identify the market conditions under which our example strategies, Goal-Directed and Derivative-Following, are successful.