Automated strategy searches in an electronic goods market: learning and complex price schedules
Proceedings of the 1st ACM conference on Electronic commerce
Competitive bundling of categorized information goods
Proceedings of the 2nd ACM conference on Electronic commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Endogenous differentiation of information goods under uncertainty
Proceedings of the 3rd ACM conference on Electronic Commerce
The Vision of Autonomic Computing
Computer
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
Mechanisms for information elicitation
Artificial Intelligence
Pick-a-bundle: a novel bundling strategy for selling multiple items within online auctions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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We explore a scenario in which a monopolist producer of information goods seeks to maximize its profits in a market where consumer demand shifts frequently and unpredictably. The producer may set an arbitrarily complex price schedule---a function that maps the set of purchased items to a price. However, lacking direct knowledge of consumer demand, it cannotcompute the optimal schedule. Instead, it attempts to optimize profits via trial and error. By means of a simple model of consumer demand and a modified version of a simple nonlinear optimization routine, we study a variety of parametrizations of the price schedule and quantify some of the relationships among learnability, complexity, and profitability. In particular, we show that fixed pricing or simple two-parameter dynamic pricing schedules are preferred when demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when demand shifts very infrequently.