17,729 research outputs found
FlexAuc: Serving Dynamic Demands in a Spectrum Trading Market with Flexible Auction
In secondary spectrum trading markets, auctions are widely used by spectrum
holders (SHs) to redistribute their unused channels to secondary wireless
service providers (WSPs). As sellers, the SHs design proper auction schemes to
stimulate more participants and maximize the revenue from the auction. As
buyers, the WSPs determine the bidding strategies in the auction to better
serve their end users.
In this paper, we consider a three-layered spectrum trading market consisting
of the SH, the WSPs and the end users. We jointly study the strategies of the
three parties. The SH determines the auction scheme and spectrum supplies to
optimize its revenue. The WSPs have flexible bidding strategies in terms of
both demands and valuations considering the strategies of the end users. We
design FlexAuc, a novel auction mechanism for this market to enable dynamic
supplies and demands in the auction. We prove theoretically that FlexAuc not
only maximizes the social welfare but also preserves other nice properties such
as truthfulness and computational tractability.Comment: 11 pages, 7 figures, Preliminary version accepted in INFOCOM 201
Approximate Revenue Maximization with Multiple Items
Maximizing the revenue from selling _more than one_ good (or item) to a
single buyer is a notoriously difficult problem, in stark contrast to the
one-good case. For two goods, we show that simple "one-dimensional" mechanisms,
such as selling the goods separately, _guarantee_ at least 73% of the optimal
revenue when the valuations of the two goods are independent and identically
distributed, and at least when they are independent. For the case of
independent goods, we show that selling them separately guarantees at
least a fraction of the optimal revenue; and, for independent and
identically distributed goods, we show that selling them as one bundle
guarantees at least a fraction of the optimal revenue. Additional
results compare the revenues from the two simple mechanisms of selling the
goods separately and bundled, identify situations where bundling is optimal,
and extend the analysis to multiple buyers.Comment: Presented in ACM EC conference, 201
On the Complexity of Dynamic Mechanism Design
We introduce a dynamic mechanism design problem in which the designer wants
to offer for sale an item to an agent, and another item to the same agent at
some point in the future. The agent's joint distribution of valuations for the
two items is known, and the agent knows the valuation for the current item (but
not for the one in the future). The designer seeks to maximize expected
revenue, and the auction must be deterministic, truthful, and ex post
individually rational. The optimum mechanism involves a protocol whereby the
seller elicits the buyer's current valuation, and based on the bid makes two
take-it-or-leave-it offers, one for now and one for the future. We show that
finding the optimum deterministic mechanism in this situation - arguably the
simplest meaningful dynamic mechanism design problem imaginable - is NP-hard.
We also prove several positive results, among them a polynomial linear
programming-based algorithm for the optimum randomized auction (even for many
bidders and periods), and we show strong separations in revenue between
non-adaptive, adaptive, and randomized auctions, even when the valuations in
the two periods are uncorrelated. Finally, for the same problem in an
environment in which contracts cannot be enforced, and thus perfection of
equilibrium is necessary, we show that the optimum randomized mechanism
requires multiple rounds of cheap talk-like interactions
Optimal Auctions vs. Anonymous Pricing: Beyond Linear Utility
The revenue optimal mechanism for selling a single item to agents with
independent but non-identically distributed values is complex for agents with
linear utility (Myerson,1981) and has no closed-form characterization for
agents with non-linear utility (cf. Alaei et al., 2012). Nonetheless, for
linear utility agents satisfying a natural regularity property, Alaei et al.
(2018) showed that simply posting an anonymous price is an e-approximation. We
give a parameterization of the regularity property that extends to agents with
non-linear utility and show that the approximation bound of anonymous pricing
for regular agents approximately extends to agents that satisfy this
approximate regularity property. We apply this approximation framework to prove
that anonymous pricing is a constant approximation to the revenue optimal
single-item auction for agents with public-budget utility, private-budget
utility, and (a special case of) risk-averse utility.Comment: Appeared at EC 201
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