8 research outputs found
On the Complexity of Computing an Equilibrium in Combinatorial Auctions
We study combinatorial auctions where each item is sold separately but
simultaneously via a second price auction. We ask whether it is possible to
efficiently compute in this game a pure Nash equilibrium with social welfare
close to the optimal one.
We show that when the valuations of the bidders are submodular, in many
interesting settings (e.g., constant number of bidders, budget additive
bidders) computing an equilibrium with good welfare is essentially as easy as
computing, completely ignoring incentives issues, an allocation with good
welfare. On the other hand, for subadditive valuations, we show that computing
an equilibrium requires exponential communication. Finally, for XOS (a.k.a.
fractionally subadditive) valuations, we show that if there exists an efficient
algorithm that finds an equilibrium, it must use techniques that are very
different from our current ones
Truthful Multi-unit Procurements with Budgets
We study procurement games where each seller supplies multiple units of his
item, with a cost per unit known only to him. The buyer can purchase any number
of units from each seller, values different combinations of the items
differently, and has a budget for his total payment.
For a special class of procurement games, the {\em bounded knapsack} problem,
we show that no universally truthful budget-feasible mechanism can approximate
the optimal value of the buyer within , where is the total number of
units of all items available. We then construct a polynomial-time mechanism
that gives a -approximation for procurement games with {\em concave
additive valuations}, which include bounded knapsack as a special case. Our
mechanism is thus optimal up to a constant factor. Moreover, for the bounded
knapsack problem, given the well-known FPTAS, our results imply there is a
provable gap between the optimization domain and the mechanism design domain.
Finally, for procurement games with {\em sub-additive valuations}, we
construct a universally truthful budget-feasible mechanism that gives an
-approximation in polynomial time with a
demand oracle.Comment: To appear at WINE 201
Budget Feasible Mechanism Design: From Prior-Free to Bayesian
Budget feasible mechanism design studies procurement combinatorial auctions
where the sellers have private costs to produce items, and the
buyer(auctioneer) aims to maximize a social valuation function on subsets of
items, under the budget constraint on the total payment. One of the most
important questions in the field is "which valuation domains admit truthful
budget feasible mechanisms with `small' approximations (compared to the social
optimum)?" Singer showed that additive and submodular functions have such
constant approximations. Recently, Dobzinski, Papadimitriou, and Singer gave an
O(log^2 n)-approximation mechanism for subadditive functions; they also
remarked that: "A fundamental question is whether, regardless of computational
constraints, a constant-factor budget feasible mechanism exists for subadditive
functions."
We address this question from two viewpoints: prior-free worst case analysis
and Bayesian analysis. For the prior-free framework, we use an LP that
describes the fractional cover of the valuation function; it is also connected
to the concept of approximate core in cooperative game theory. We provide an
O(I)-approximation mechanism for subadditive functions, via the worst case
integrality gap I of LP. This implies an O(log n)-approximation for subadditive
valuations, O(1)-approximation for XOS valuations, and for valuations with a
constant I. XOS valuations are an important class of functions that lie between
submodular and subadditive classes. We give another polynomial time O(log
n/loglog n) sub-logarithmic approximation mechanism for subadditive valuations.
For the Bayesian framework, we provide a constant approximation mechanism for
all subadditive functions, using the above prior-free mechanism for XOS
valuations as a subroutine. Our mechanism allows correlations in the
distribution of private information and is universally truthful.Comment: to appear in STOC 201
Computational Efficiency Requires Simple Taxation
We characterize the communication complexity of truthful mechanisms. Our
departure point is the well known taxation principle. The taxation principle
asserts that every truthful mechanism can be interpreted as follows: every
player is presented with a menu that consists of a price for each bundle (the
prices depend only on the valuations of the other players). Each player is
allocated a bundle that maximizes his profit according to this menu. We define
the taxation complexity of a truthful mechanism to be the logarithm of the
maximum number of menus that may be presented to a player.
Our main finding is that in general the taxation complexity essentially
equals the communication complexity. The proof consists of two main steps.
First, we prove that for rich enough domains the taxation complexity is at most
the communication complexity. We then show that the taxation complexity is much
smaller than the communication complexity only in "pathological" cases and
provide a formal description of these extreme cases.
Next, we study mechanisms that access the valuations via value queries only.
In this setting we establish that the menu complexity -- a notion that was
already studied in several different contexts -- characterizes the number of
value queries that the mechanism makes in exactly the same way that the
taxation complexity characterizes the communication complexity.
Our approach yields several applications, including strengthening the
solution concept with low communication overhead, fast computation of prices,
and hardness of approximation by computationally efficient truthful mechanisms