Dynamics of complex systems
Dynamic pricing by software agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
Reinforcement Learning
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Computers play the beer game: can artificial agents manage supply chains?
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents
Electronic Commerce Research
On No-Regret Learning, Fictitious Play, and Nash Equilibrium
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Competition Among Sellers in Online Exchanges
Information Systems Research
Simulating sellers in online exchanges
Decision Support Systems
An agent-based decision support system for wholesale electricity market
Decision Support Systems
IEEE Transactions on Evolutionary Computation
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (rational behavior) and should demonstrate low variance in profits (convergence). We present rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of rational behavior and convergence.