13 research outputs found
Budget-Feasible Mechanism Design for Non-Monotone Submodular Objectives: Offline and Online
The framework of budget-feasible mechanism design studies procurement
auctions where the auctioneer (buyer) aims to maximize his valuation function
subject to a hard budget constraint. We study the problem of designing truthful
mechanisms that have good approximation guarantees and never pay the
participating agents (sellers) more than the budget. We focus on the case of
general (non-monotone) submodular valuation functions and derive the first
truthful, budget-feasible and -approximate mechanisms that run in
polynomial time in the value query model, for both offline and online auctions.
Prior to our work, the only -approximation mechanism known for
non-monotone submodular objectives required an exponential number of value
queries.
At the heart of our approach lies a novel greedy algorithm for non-monotone
submodular maximization under a knapsack constraint. Our algorithm builds two
candidate solutions simultaneously (to achieve a good approximation), yet
ensures that agents cannot jump from one solution to the other (to implicitly
enforce truthfulness). Ours is the first mechanism for the problem
where---crucially---the agents are not ordered with respect to their marginal
value per cost. This allows us to appropriately adapt these ideas to the online
setting as well.
To further illustrate the applicability of our approach, we also consider the
case where additional feasibility constraints are present. We obtain
-approximation mechanisms for both monotone and non-monotone submodular
objectives, when the feasible solutions are independent sets of a -system.
With the exception of additive valuation functions, no mechanisms were known
for this setting prior to our work. Finally, we provide lower bounds suggesting
that, when one cares about non-trivial approximation guarantees in polynomial
time, our results are asymptotically best possible.Comment: Accepted to EC 201
On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives
We study a class of procurement auctions with a budget constraint, where an
auctioneer is interested in buying resources or services from a set of agents.
Ideally, the auctioneer would like to select a subset of the resources so as to
maximize his valuation function, without exceeding a given budget. As the
resources are owned by strategic agents however, our overall goal is to design
mechanisms that are truthful, budget-feasible, and obtain a good approximation
to the optimal value. Budget-feasibility creates additional challenges, making
several approaches inapplicable in this setting. Previous results on
budget-feasible mechanisms have considered mostly monotone valuation functions.
In this work, we mainly focus on symmetric submodular valuations, a prominent
class of non-monotone submodular functions that includes cut functions. We
begin first with a purely algorithmic result, obtaining a
-approximation for maximizing symmetric submodular functions
under a budget constraint. We view this as a standalone result of independent
interest, as it is the best known factor achieved by a deterministic algorithm.
We then proceed to propose truthful, budget feasible mechanisms (both
deterministic and randomized), paying particular attention on the Budgeted Max
Cut problem. Our results significantly improve the known approximation ratios
for these objectives, while establishing polynomial running time for cases
where only exponential mechanisms were known. At the heart of our approach lies
an appropriate combination of local search algorithms with results for monotone
submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201
A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders
We study incentive compatible mechanisms for Combinatorial Auctions where the
bidders have submodular (or XOS) valuations and are budget-constrained. Our
objective is to maximize the \emph{liquid welfare}, a notion of efficiency for
budget-constrained bidders introduced by Dobzinski and Paes Leme (2014). We
show that some of the known truthful mechanisms that best-approximate the
social welfare for Combinatorial Auctions with submodular bidders through
demand query oracles can be adapted, so that they retain truthfulness and
achieve asymptotically the same approximation guarantees for the liquid
welfare. More specifically, for the problem of optimizing the liquid welfare in
Combinatorial Auctions with submodular bidders, we obtain a universally
truthful randomized -approximate mechanism, where is the number
of items, by adapting the mechanism of Krysta and V\"ocking (2012).
Additionally, motivated by large market assumptions often used in mechanism
design, we introduce a notion of competitive markets and show that in such
markets, liquid welfare can be approximated within a constant factor by a
randomized universally truthful mechanism. Finally, in the Bayesian setting, we
obtain a truthful -approximate mechanism for the case where bidder
valuations are generated as independent samples from a known distribution, by
adapting the results of Feldman, Gravin and Lucier (2014).Comment: AAAI-1
Optimal Scoring Rules for Multi-dimensional Effort
This paper develops a framework for the design of scoring rules to optimally
incentivize an agent to exert a multi-dimensional effort. This framework is a
generalization to strategic agents of the classical knapsack problem (cf.
Briest, Krysta, and V\"ocking, 2005, Singer, 2010) and it is foundational to
applying algorithmic mechanism design to the classroom. The paper identifies
two simple families of scoring rules that guarantee constant approximations to
the optimal scoring rule. The truncated separate scoring rule is the sum of
single dimensional scoring rules that is truncated to the bounded range of
feasible scores. The threshold scoring rule gives the maximum score if reports
exceed a threshold and zero otherwise. Approximate optimality of one or the
other of these rules is similar to the bundling or selling separately result of
Babaioff, Immorlica, Lucier, and Weinberg (2014). Finally, we show that the
approximate optimality of the best of those two simple scoring rules is robust
when the agent's choice of effort is made sequentially
Budget-Smoothed Analysis for Submodular Maximization
The greedy algorithm for monotone submodular function maximization subject to cardinality constraint is guaranteed to approximate the optimal solution to within a 1-1/e factor. Although it is well known that this guarantee is essentially tight in the worst case - for greedy and in fact any efficient algorithm, experiments show that greedy performs better in practice. We observe that for many applications in practice, the empirical distribution of the budgets (i.e., cardinality constraints) is supported on a wide range, and moreover, all the existing hardness results in theory break under a large perturbation of the budget.
To understand the effect of the budget from both algorithmic and hardness perspectives, we introduce a new notion of budget-smoothed analysis. We prove that greedy is optimal for every budget distribution, and we give a characterization for the worst-case submodular functions. Based on these results, we show that on the algorithmic side, under realistic budget distributions, greedy and related algorithms enjoy provably better approximation guarantees, that hold even for worst-case functions, and on the hardness side, there exist hard functions that are fairly robust to all the budget distributions