Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Computing pure Bayesian-Nash equilibria in games with finite actions and continuous types
Artificial Intelligence
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Current autonomous bidding strategies for complex auctions typically employ a two phased architecture: first, the agent predicts a distribution over good prices, and then the agent generates bids given those predictions, usually using a heuristic. For computational reasons, previous state-of-the-art methods assumed prices were independent across goods, and then bid based on marginal price distributions. However, prices for goods are typically dependent, especially for complements and substitutes. We examine and bound the potential error from bidding with respect to marginal price distributions when good prices are in fact correlated. Then, to mitigate this error, we develop computationally feasible methods for predicting joint price distributions, and employing such predictions in bidding strategies. We also demonstrate experimentally that the state-of-the-art heuristic for bidding in simultaneous second-price sealed-bid auctions is outdone by the analog of this same heuristic bidding with respect to joint instead of marginal price predictions.