433 research outputs found
Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension
Algorithmic mechanism design (AMD) studies the delicate interplay between
computational efficiency, truthfulness, and optimality. We focus on AMD's
paradigmatic problem: combinatorial auctions. We present a new generalization
of the VC dimension to multivalued collections of functions, which encompasses
the classical VC dimension, Natarajan dimension, and Steele dimension. We
present a corresponding generalization of the Sauer-Shelah Lemma and harness
this VC machinery to establish inapproximability results for deterministic
truthful mechanisms. Our results essentially unify all inapproximability
results for deterministic truthful mechanisms for combinatorial auctions to
date and establish new separation gaps between truthful and non-truthful
algorithms
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
Mechanisms for Risk Averse Agents, Without Loss
Auctions in which agents' payoffs are random variables have received
increased attention in recent years. In particular, recent work in algorithmic
mechanism design has produced mechanisms employing internal randomization,
partly in response to limitations on deterministic mechanisms imposed by
computational complexity. For many of these mechanisms, which are often
referred to as truthful-in-expectation, incentive compatibility is contingent
on the assumption that agents are risk-neutral. These mechanisms have been
criticized on the grounds that this assumption is too strong, because "real"
agents are typically risk averse, and moreover their precise attitude towards
risk is typically unknown a-priori. In response, researchers in algorithmic
mechanism design have sought the design of universally-truthful mechanisms ---
mechanisms for which incentive-compatibility makes no assumptions regarding
agents' attitudes towards risk.
We show that any truthful-in-expectation mechanism can be generically
transformed into a mechanism that is incentive compatible even when agents are
risk averse, without modifying the mechanism's allocation rule. The transformed
mechanism does not require reporting of agents' risk profiles. Equivalently,
our result can be stated as follows: Every (randomized) allocation rule that is
implementable in dominant strategies when players are risk neutral is also
implementable when players are endowed with an arbitrary and unknown concave
utility function for money.Comment: Presented at the workshop on risk aversion in algorithmic game theory
and mechanism design, held in conjunction with EC 201
Single Parameter Combinatorial Auctions with Partially Public Valuations
We consider the problem of designing truthful auctions, when the bidders'
valuations have a public and a private component. In particular, we consider
combinatorial auctions where the valuation of an agent for a set of
items can be expressed as , where is a private single parameter
of the agent, and the function is publicly known. Our motivation behind
studying this problem is two-fold: (a) Such valuation functions arise naturally
in the case of ad-slots in broadcast media such as Television and Radio. For an
ad shown in a set of ad-slots, is, say, the number of {\em unique}
viewers reached by the ad, and is the valuation per-unique-viewer. (b)
From a theoretical point of view, this factorization of the valuation function
simplifies the bidding language, and renders the combinatorial auction more
amenable to better approximation factors. We present a general technique, based
on maximal-in-range mechanisms, that converts any -approximation
non-truthful algorithm () for this problem into
and -approximate truthful
mechanisms which run in polynomial time and quasi-polynomial time,
respectively
On Simultaneous Two-player Combinatorial Auctions
We consider the following communication problem: Alice and Bob each have some
valuation functions and over subsets of items,
and their goal is to partition the items into in a way that
maximizes the welfare, . We study both the allocation
problem, which asks for a welfare-maximizing partition and the decision
problem, which asks whether or not there exists a partition guaranteeing
certain welfare, for binary XOS valuations. For interactive protocols with
communication, a tight 3/4-approximation is known for both
[Fei06,DS06].
For interactive protocols, the allocation problem is provably harder than the
decision problem: any solution to the allocation problem implies a solution to
the decision problem with one additional round and additional bits of
communication via a trivial reduction. Surprisingly, the allocation problem is
provably easier for simultaneous protocols. Specifically, we show:
1) There exists a simultaneous, randomized protocol with polynomial
communication that selects a partition whose expected welfare is at least
of the optimum. This matches the guarantee of the best interactive, randomized
protocol with polynomial communication.
2) For all , any simultaneous, randomized protocol that
decides whether the welfare of the optimal partition is or correctly with probability requires
exponential communication. This provides a separation between the attainable
approximation guarantees via interactive () versus simultaneous () protocols with polynomial communication.
In other words, this trivial reduction from decision to allocation problems
provably requires the extra round of communication
Implementation in Advised Strategies: Welfare Guarantees from Posted-Price Mechanisms When Demand Queries Are NP-Hard
State-of-the-art posted-price mechanisms for submodular bidders with
items achieve approximation guarantees of [Assadi and
Singla, 2019]. Their truthfulness, however, requires bidders to compute an
NP-hard demand-query. Some computational complexity of this form is
unavoidable, as it is NP-hard for truthful mechanisms to guarantee even an
-approximation for any [Dobzinski and
Vondr\'ak, 2016]. Together, these establish a stark distinction between
computationally-efficient and communication-efficient truthful mechanisms.
We show that this distinction disappears with a mild relaxation of
truthfulness, which we term implementation in advised strategies, and that has
been previously studied in relation to "Implementation in Undominated
Strategies" [Babaioff et al, 2009]. Specifically, advice maps a tentative
strategy either to that same strategy itself, or one that dominates it. We say
that a player follows advice as long as they never play actions which are
dominated by advice. A poly-time mechanism guarantees an -approximation
in implementation in advised strategies if there exists poly-time advice for
each player such that an -approximation is achieved whenever all
players follow advice. Using an appropriate bicriterion notion of approximate
demand queries (which can be computed in poly-time), we establish that (a
slight modification of) the [Assadi and Singla, 2019] mechanism achieves the
same -approximation in implementation in advised
strategies
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