On Revenue Monotonicity in Combinatorial Auctions
Andrew Chi-Chih Yao

TL;DR
This paper proves an approximate revenue monotonicity result for combinatorial auctions with fractionally subadditive valuations, showing that higher valuation distributions generally lead to higher revenue within a constant factor.
Contribution
It establishes the first approximate monotonicity theorem for multi-buyer combinatorial auctions with XOS valuations, extending prior results limited to single-buyer or subadditive cases.
Findings
Revenue from higher valuation distributions is at least a constant factor of the lower valuation case.
Approximate monotonicity holds for XOS valuation functions in multi-buyer settings.
The constant factor c is universal, independent of the specific distributions or valuations.
Abstract
Along with substantial progress made recently in designing near-optimal mechanisms for multi-item auctions, interesting structural questions have also been raised and studied. In particular, is it true that the seller can always extract more revenue from a market where the buyers value the items higher than another market? In this paper we obtain such a revenue monotonicity result in a general setting. Precisely, consider the revenue-maximizing combinatorial auction for items and buyers in the Bayesian setting, specified by a valuation function and a set of independent item-type distributions. Let denote the maximum revenue achievable under by any incentive compatible mechanism. Intuitively, one would expect that if distribution stochastically dominates . Surprisingly, Hart and Reny (2012) showed that this is not…
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