Probabilistic reasoning in intelligent systems: networks of plausible inference
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Efficient mechanisms for the supply of services in multi-agent environments
Decision Support Systems - Special issue on information and computational economics
The Future of Emarkets: Multi-Dimensional Market Mechanisms
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Statistics and Computing
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UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
An English Auction Protocol for Multi-attribute Items
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Modeling complex multi-issue negotiations using utility graphs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Bid expressiveness and clearing algorithms in multiattribute double auctions
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Models for Iterative Multiattribute Procurement Auctions
Management Science
Generalized value decomposition and structured multiattribute auctions
Proceedings of the 8th ACM conference on Electronic commerce
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Structured preference representation and multiattribute auctions
Structured preference representation and multiattribute auctions
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Journal of Artificial Intelligence Research
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We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions.