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Combinatorial spectrum auctions have been some of the biggest
successes of auction design over the last 20 years. However, designing
an optimal payment rule still remains a challenging
problem. Unfortunately, in domains with complementarities, the classic
VCG mechanism often produces prices outside the core. Informally, this
means that VCG prices could be too low, such that some bidders would
be willing to pay more than what the winners are paying. For this
reason, recent auctions have employed \emph{core-selecting} payment
rules. Day and Raghavan (2007) have argued that the core
provides a fairness guarantee in that the winners' payments will be at
least as high as the sum of the bids of any other coalition of
bidders. However, we argue that the core primarily provides a fairness
guarantee to losing bidders and says little about how payoffs are
distributed among the winners.
In this paper, we propose an additional notion of fairness that goes
beyond the core. We consider the fact that auction participants
are often heterogeneous in size and value, and we study the
distribution of winners' payoffs. We find that the most common pricing
rule that has been used in practice, the Quadratic rule
(Day and Cramton, 2012),
is unfair towards small players participating in the auction. In
contrast to prior work, which has only studied the Quadratic rule in
stylized settings, we use computational methods to study approximate
Bayes-Nash equilibria of payment rules in more complex settings. We
propose alternative, fairer payment rules that improve upon the
Quadratic rule while retaining its high efficiency. Our new rules
involve novel bidder weightings, an ε-relaxation of the
core, and new distance metrics to VCG prices. To find a payment rule
that strikes an optimal trade-off between all design objectives
(efficiency, core, fairness, revenue, and incentives), we have
evaluated over 100 different payment rules via our computational
Bayes-Nash equilibrium analysis.[pdf]