Results 1  10
of
22
Item Pricing for Revenue Maximization
"... We consider the problem of pricing n items to maximize revenue when faced with a series of unknown buyers with complex preferences, and show that a simple pricing scheme achieves surprisingly strong guarantees. We show that in the unlimited supply setting, a random single price achieves expected rev ..."
Abstract

Cited by 41 (6 self)
 Add to MetaCart
(Show Context)
We consider the problem of pricing n items to maximize revenue when faced with a series of unknown buyers with complex preferences, and show that a simple pricing scheme achieves surprisingly strong guarantees. We show that in the unlimited supply setting, a random single price achieves expected revenue within a logarithmic factor of the total social welfare for customers with general valuation functions, which may not even necessarily be monotone. This generalizes work of Guruswami et. al [18], who show a logarithmic factor for only the special cases of singleminded and unitdemand customers. In the limited supply setting, we show that for subadditive valuations, a random single price achieves revenue within a factor of 2 O( √ log n log log n) of the total social welfare, i.e., the optimal revenue the seller could hope to extract even if the seller could price each bundle differently for every buyer. This is the best approximation known for any item pricing scheme for subadditive (or even submodular) valuations, even using multiple prices. We complement this result with a lower bound showing a sequence of subadditive (in fact, XOS) buyers for which any single price has approximation ratio 2 Ω(log1/4 n), thus showing that single price schemes cannot achieve a polylogarithmic ratio. This lower bound demonstrates a clear distinction between revenue maximization and social welfare maximization in this setting, for which [12, 10] show that a fixed price achieves a logarithmic approximation in the case of XOS [12], and more generally subadditive [10], customers.
Singlevalue combinatorial auctions and algorithmic implementation in undominated strategies
 In ACM Symposium on Discrete Algorithms
, 2011
"... In this paper we are interested in general techniques for designing mechanisms that approximate the social welfare in the presence of selfish rational behavior. We demonstrate our results in the setting of Combinatorial Auctions (CA). Our first result is a general deterministic technique to decouple ..."
Abstract

Cited by 26 (2 self)
 Add to MetaCart
(Show Context)
In this paper we are interested in general techniques for designing mechanisms that approximate the social welfare in the presence of selfish rational behavior. We demonstrate our results in the setting of Combinatorial Auctions (CA). Our first result is a general deterministic technique to decouple the algorithmic allocation problem from the strategic aspects, by a procedure that converts any algorithm to a dominantstrategy ascending mechanism. This technique works for any single value domain, in which each agent has the same value for each desired outcome, and this value is the only private information. In particular, for “singlevalue CAs”, where each player desires any one of several different bundles but has the same value for each of them, our technique converts any approximation algorithm to a dominant strategy mechanism that almost preserves the original approximation ratio. Our second result provides the first computationally efficient deterministic mechanism for the case of singlevalue multiminded bidders (with private value and private desired bundles). The mechanism achieves an approximation to the social welfare which is close to the best possible in polynomial time (unless P=NP). This mechanism is an algorithmic implementation in undominated strategies, a notion that we define and justify, and is of independent interest. 1
Computing equilibria: A computational complexity perspective
, 2009
"... Computational complexity is the subfield of computer science that rigorously studies the intrinsic difficulty of computational problems. This survey explains how complexity theory defines “hard problems”; applies these concepts to several equilibrium computation problems; and discusses implications ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
(Show Context)
Computational complexity is the subfield of computer science that rigorously studies the intrinsic difficulty of computational problems. This survey explains how complexity theory defines “hard problems”; applies these concepts to several equilibrium computation problems; and discusses implications for computation, games, and behavior. We assume
Is Shapley Cost Sharing Optimal?
"... Abstract. We study the best guarantees of efficiency approximation achievable by costsharing mechanisms. Our main result is the first quantitative lower bound that applies to all truthful costsharing mechanisms, including randomized mechanisms that are only truthful in expectation, and only βbudg ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We study the best guarantees of efficiency approximation achievable by costsharing mechanisms. Our main result is the first quantitative lower bound that applies to all truthful costsharing mechanisms, including randomized mechanisms that are only truthful in expectation, and only βbudgetbalanced in expectation. Our lower bound is optimal up to constant factors and applies even to the simple and central special case of the public excludable good problem. We also give a stronger lower bound for a subclass of deterministic costsharing mechanisms, which is driven by a new characterization of the Shapley value mechanism. Finally, we show a separation between the bestpossible efficiency guarantees achievable by deterministic and randomized costsharing mechanisms.
Truthful Mechanisms via Greedy Iterative Packing
, 2009
"... An important research thread in algorithmic game theory studies the design of efficient truthful mechanisms that approximate the optimal social welfare. A fundamental question is whether an αapproximation algorithm translates into an αapproximate truthful mechanism. It is wellknown that plugging ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
An important research thread in algorithmic game theory studies the design of efficient truthful mechanisms that approximate the optimal social welfare. A fundamental question is whether an αapproximation algorithm translates into an αapproximate truthful mechanism. It is wellknown that plugging an αapproximation algorithm into the VCG technique may not yield a truthful mechanism. Hence, it is natural to investigate properties of approximation algorithms that enable their use in truthful mechanisms. The main contribution of this paper is to identify a useful and natural property of approximation algorithms, which we call loserindependence. Intuitively, a loserindependent algorithm does not change its outcome when the bid of a losing agent increases, unless that agent becomes a winner. We demonstrate that loserindependent algorithms can be employed as subprocedures in a greedy iterative packing approach while preserving monotonicity. A greedy iterative approach provides good approximation in the context of maximizing a nondecreasing submodular function subject to independence constraints. Our framework gives rise to truthful approximation mechanisms for various problems. Notably, some problems arise in online mechanism design.
A survey of approximability and inapproximability results for social welfare optimization in multiagent resource allocation
 In Website Proceedings of the special session on Computational Social Choice at the 12th International Symposium on Artificial Intelligence and Mathematics
"... We survey recent approximability and inapproximability results on social welfare optimization in multiagent resource allocation, focusing on the two most central representation forms for utility functions of agents, the bundle form and the kadditive form. In addition, we provide some new (in)appr ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
We survey recent approximability and inapproximability results on social welfare optimization in multiagent resource allocation, focusing on the two most central representation forms for utility functions of agents, the bundle form and the kadditive form. In addition, we provide some new (in)approximability results on maximizing egalitarian social welfare and social welfare with respect to the Nash product when restricted to certain special cases.
An algorithmic game theory primer
, 2008
"... We give a brief and biased survey of the past, present, and future of research on the interface of theoretical computer science and game theory. 1 ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
We give a brief and biased survey of the past, present, and future of research on the interface of theoretical computer science and game theory. 1
Efficient grid taskbundle allocation using bargaining based selfadaptive auction
 in IEEE International Symposium on Cluster Computing and the Grid (CCGrid 09
"... Abstract—In this paper, to address coordination and complexity issues, we formulate a grid task allocation problem as a bargaining based selfadaptive auction and propose the BarSAA grid taskbundle allocation algorithm. During the auction, prices are iteratively negotiated and dynamically adjusted ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, to address coordination and complexity issues, we formulate a grid task allocation problem as a bargaining based selfadaptive auction and propose the BarSAA grid taskbundle allocation algorithm. During the auction, prices are iteratively negotiated and dynamically adjusted until market equilibrium is reached. The BarSAA algorithm features decentralized bidding decision making in a heterogeneous distributed environment so that scheduler can offload its duty onto participating computing nodes and significantly reduces scheduling overheads. When a BarSAA auction converges, the equilibrium point is Pareto Optimal and achieves social efficient outcome and doublesided revenue maximization. In addition, BarSAA promotes truthful behavior among selfish nodes. Through game theoretical analysis, we demonstrate that truthful revelation is beneficial to bidders in making bidding strategies. Extensive simulation results are presented to demonstrate the efficiency of the BarSAA strategy and validate several important analytical properties. I.
Welfare and Profit Maximization with Production Costs
, 2011
"... Combinatorial Auctions are a central problem in Algorithmic Mechanism Design: pricing and allocating goods to buyers with complex preferences in order to maximize some desired objective (e.g., social welfare, revenue, or profit). The problem has been wellstudied in the case of limited supply (one c ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Combinatorial Auctions are a central problem in Algorithmic Mechanism Design: pricing and allocating goods to buyers with complex preferences in order to maximize some desired objective (e.g., social welfare, revenue, or profit). The problem has been wellstudied in the case of limited supply (one copy of each item), and in the case of digital goods (the seller can produce additional copies at no cost). Yet in the case of resources—oil, labor, computing cycles, etc.—neither of these abstractions is just right: additional supplies of these resources can be found, but at increasing difficulty (marginal cost) as resources are depleted. In this work, we initiate the study of the algorithmic mechanism design problem of combinatorial pricing under increasing marginal cost. The goal is to sell these goods to buyers with unknown and arbitrary combinatorial valuation functions to maximize either the social welfare, or the seller’s profit; specifically we focus on the setting of posted item prices with buyers arriving online. We give algorithms that achieve constant factor approximations for a class of natural cost functions—linear, lowdegree polynomial, logarithmic—and that give logarithmic approximations for arbitrary increasing marginal cost functions (along with a necessary additive loss). We show that these bounds are essentially best possible for these settings.
Efficiency with Linear Prices? A Theoretical and Experimental Analysis of the Combinatorial Clock Auction.
"... Combinatorial auctions have been suggested as a means to raise efficiency in multiitem negotiations with complementarities among goods as they can be found in procurement, energy markets, transportation, and the sale of spectrum auctions. The Combinatorial Clock (CC) auction has become very popular ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Combinatorial auctions have been suggested as a means to raise efficiency in multiitem negotiations with complementarities among goods as they can be found in procurement, energy markets, transportation, and the sale of spectrum auctions. The Combinatorial Clock (CC) auction has become very popular in these markets for its simplicity and for its highly usable price discovery, derived by the use of linear prices. Unfortunately, no equilibrium bidding strategies are known. Given the importance of the CC auction in the field, it is highly desirable to understand whether there are efficient versions of the CC auction, providing a strong game theoretical solution concept. So far, equilibrium strategies have only been found for combinatorial auctions with nonlinear and personalized prices for very restricted sets of bidder valuations. We provide an extension of the CC auction, the CC+ auction, and show that it actually leads to efficient outcomes in an expost equilibrium for general valuations with only linear ask prices. We also provide a theoretical analysis on the worst case efficiency of the CC auction, which highlights problems in the valuations, in which the CC is very inefficient. As in all other theoretical models of combinatorial auctions, bidders in the field might not be able to follow the equilibrium strategies suggested by the gametheoretical predictions. Therefore, we complement the theoretical findings with results from computational experiments using realistic value models. This analysis helps to understand the impact of deviations from the equilibrium strategy and the robustness of such auctions. The experimental analysis shows that the CC auction and its extensions have a number of virtues in practical applications, in particular a low number of auction rounds and bids submitted compared to auction designs with nonlinear and personalized ask prices. Key words: electronic markets and auctions, combinatorial clock auction, allocative efficiency, coreselecting auctions