453 research outputs found
Computing Stable Coalitions: Approximation Algorithms for Reward Sharing
Consider a setting where selfish agents are to be assigned to coalitions or
projects from a fixed set P. Each project k is characterized by a valuation
function; v_k(S) is the value generated by a set S of agents working on project
k. We study the following classic problem in this setting: "how should the
agents divide the value that they collectively create?". One traditional
approach in cooperative game theory is to study core stability with the
implicit assumption that there are infinite copies of one project, and agents
can partition themselves into any number of coalitions. In contrast, we
consider a model with a finite number of non-identical projects; this makes
computing both high-welfare solutions and core payments highly non-trivial.
The main contribution of this paper is a black-box mechanism that reduces the
problem of computing a near-optimal core stable solution to the purely
algorithmic problem of welfare maximization; we apply this to compute an
approximately core stable solution that extracts one-fourth of the optimal
social welfare for the class of subadditive valuations. We also show much
stronger results for several popular sub-classes: anonymous, fractionally
subadditive, and submodular valuations, as well as provide new approximation
algorithms for welfare maximization with anonymous functions. Finally, we
establish a connection between our setting and the well-studied simultaneous
auctions with item bidding; we adapt our results to compute approximate pure
Nash equilibria for these auctions.Comment: Under Revie
On the Efficiency of the Proportional Allocation Mechanism for Divisible Resources
We study the efficiency of the proportional allocation mechanism, that is
widely used to allocate divisible resources. Each agent submits a bid for each
divisible resource and receives a fraction proportional to her bids. We
quantify the inefficiency of Nash equilibria by studying the Price of Anarchy
(PoA) of the induced game under complete and incomplete information. When
agents' valuations are concave, we show that the Bayesian Nash equilibria can
be arbitrarily inefficient, in contrast to the well-known 4/3 bound for pure
equilibria. Next, we upper bound the PoA over Bayesian equilibria by 2 when
agents' valuations are subadditive, generalizing and strengthening previous
bounds on lattice submodular valuations. Furthermore, we show that this bound
is tight and cannot be improved by any simple or scale-free mechanism. Then we
switch to settings with budget constraints, and we show an improved upper bound
on the PoA over coarse-correlated equilibria. Finally, we prove that the PoA is
exactly 2 for pure equilibria in the polyhedral environment.Comment: To appear in SAGT 201
Pricing Multi-Unit Markets
We study the power and limitations of posted prices in multi-unit markets,
where agents arrive sequentially in an arbitrary order. We prove upper and
lower bounds on the largest fraction of the optimal social welfare that can be
guaranteed with posted prices, under a range of assumptions about the
designer's information and agents' valuations. Our results provide insights
about the relative power of uniform and non-uniform prices, the relative
difficulty of different valuation classes, and the implications of different
informational assumptions. Among other results, we prove constant-factor
guarantees for agents with (symmetric) subadditive valuations, even in an
incomplete-information setting and with uniform prices
Simple Mechanisms for a Subadditive Buyer and Applications to Revenue Monotonicity
We study the revenue maximization problem of a seller with n heterogeneous
items for sale to a single buyer whose valuation function for sets of items is
unknown and drawn from some distribution D. We show that if D is a distribution
over subadditive valuations with independent items, then the better of pricing
each item separately or pricing only the grand bundle achieves a
constant-factor approximation to the revenue of the optimal mechanism. This
includes buyers who are k-demand, additive up to a matroid constraint, or
additive up to constraints of any downwards-closed set system (and whose values
for the individual items are sampled independently), as well as buyers who are
fractionally subadditive with item multipliers drawn independently. Our proof
makes use of the core-tail decomposition framework developed in prior work
showing similar results for the significantly simpler class of additive buyers
[LY13, BILW14].
In the second part of the paper, we develop a connection between
approximately optimal simple mechanisms and approximate revenue monotonicity
with respect to buyers' valuations. Revenue non-monotonicity is the phenomenon
that sometimes strictly increasing buyers' values for every set can strictly
decrease the revenue of the optimal mechanism [HR12]. Using our main result, we
derive a bound on how bad this degradation can be (and dub such a bound a proof
of approximate revenue monotonicity); we further show that better bounds on
approximate monotonicity imply a better analysis of our simple mechanisms.Comment: Updated title and body to version included in TEAC. Adapted Theorem
5.2 to accommodate \eta-BIC auctions (versus exactly BIC
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
Tight Bounds for the Price of Anarchy of Simultaneous First Price Auctions
We study the Price of Anarchy of simultaneous first-price auctions for buyers
with submodular and subadditive valuations. The current best upper bounds for
the Bayesian Price of Anarchy of these auctions are e/(e-1) [Syrgkanis and
Tardos 2013] and 2 [Feldman et al. 2013], respectively. We provide matching
lower bounds for both cases even for the case of full information and for mixed
Nash equilibria via an explicit construction.
We present an alternative proof of the upper bound of e/(e-1) for first-price
auctions with fractionally subadditive valuations which reveals the worst-case
price distribution, that is used as a building block for the matching lower
bound construction.
We generalize our results to a general class of item bidding auctions that we
call bid-dependent auctions (including first-price auctions and all-pay
auctions) where the winner is always the highest bidder and each bidder's
payment depends only on his own bid.
Finally, we apply our techniques to discriminatory price multi-unit auctions.
We complement the results of [de Keijzer et al. 2013] for the case of
subadditive valuations, by providing a matching lower bound of 2. For the case
of submodular valuations, we provide a lower bound of 1.109. For the same class
of valuations, we were able to reproduce the upper bound of e/(e-1) using our
non-smooth approach.Comment: 37 pages, 5 figures, ACM Transactions on Economics and Computatio
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