We provide information-theoretic lower bounds for maximizing welfare in combinatorial auctions, suggesting new insights for item allocation strategies.
We provide tight information-theoretic lower bounds for the welfare maximization problem in combinatorial auctions. In this problem, the goal is to partition m items among k bidders in a way that maximizes the sum of bidders' values for their allocated items. Bidders have complex preferences over items expressed by valuation functions that assign values to all subsets of items.
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Mirrokni et al. (2008) studied this question.
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