Envy Quotes and the Iterated Core-Selecting Combinatorial Auction

Using a model of agent behavior based around envy-reducing strategies, we describe an iterated combinatorial auction in which the allocation and prices converge to a solution in the core of the agents’ true valuations. In each round of the iterative auction mechanism, agents act on envy quotes produced by the mechanism: hints that suggest the prices of the bundles they are interested in. We describe optimal methods of generating envy quotes for two different core-selecting mechanisms. Prior work on core-selecting combinatorial auctions has required agents to have perfect information about every agent’s valuations to achieve a solution in the core. In contrast, here a core solution is reached even in the private information setting.

Authors

• Abraham Othman U.S. Green Building Council
• Tuomas Sandholm Carnegie Mellon University, Computer Science Department, Pittsburgh, USA Strategy Robot, Inc. Strategic Machine, Inc. Optimized Markets, Inc.

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