If individuals care about their status, defined as their rank in the distribution of consumption of one “positional ” good, then the consumer’s problem is strategic as her utility depends on the consumption choices of others. In the symmetric Nash equilibrium, each individual spends an inefficientlyhighamountonthe status good. Using techniques from auction theory, we analyze the effects of exogenous changes in the distribution of income. In a richer society, almost all individuals spend more on conspicuous consumption, and individual utility is lower at each income level. In a more equal society, the poor are worse off. (JEL

We present the first design for a fully expressive iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language that is concise and expressive for CEs. Bidders specify lower and upper bounds on their value for different trades. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule ensures progress across rounds. A VCG-based payment scheme that has been shown to mitigate opportunities for bargaining and strategic behavior is used to determine final payments. The exchange is fully implemented and in a validation phase.

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We study the recognized open problem of designing revenuemaximizing combinatorial auctions. It is unsolved even for two bidders and two items for sale. Rather than pursuing the pure economic approach of attempting to characterize the optimal auction, we explore techniques for automatically modifying existing mechanisms in a way that increase expected revenue. We introduce a general family of auctions, based on bidder weighting and allocation boosting, which we call virtual valuations combinatorial auctions (VVCA). All auctions in the family are based on the Vickrey-Clarke-Groves (VCG) mechanism, executed on virtual valuations that are linear transformations of the bidders' real valuations. The restriction to linear transformations is motivated by incentive compatibility. The auction family is parameterized by the coefficients in the linear transformations. The problem

www.elsevier.com/locate/artint We study Coalitional Resource Games (CRGs), a variation of Qualitative Coalitional Games (QCGs) in which each agent is endowed with a set of resources, and the ability of a coalition to bring about a set of goals depends on whether they are collectively endowed with the necessary resources. We investigate and classify the computational complexity of a number of natural decision problems for CRGs, over and above those previously investigated for QCGs in general. For example, we show that the complexity of determining whether conflict is inevitable between two coalitions with respect to some stated resource bound (i.e., a limit value for every resource) is co-NP-complete. We then investigate the relationship between CRGs and QCGs, and in particular the extent to which it is possible to translate between the two models. We first characterise the complexity of determining equivalence between CRGs and QCGs. We then show that it is always possible to translate any given CRG into a succinct equivalent QCG, and that it is not always possible to translate a QCG into an equivalent CRG; we establish some necessary and some sufficient conditions for a translation from QCGs to CRGs to be possible, and show that even where an equivalent CRG exists, it may have size exponential in the number of goals and agents of its source QCG.

In this paper, I present new identification results and proposes an estimation method for an eBay auction model with an application. A key difficulty with data from eBay auctions is the fact that the number of potential bidders willing to pay the reserve price is not observable and the number of potential bidders varies auction by auction. While this precludes application of existing estimation methods, I show that this need not preclude structural analysis of the available bid data. In particular, I show that within the symmetric independent private values (IPV) model, observation of any two valuations of which rankings from the top is known (for example, the second- and third-highest valuations) nonparametrically identifies the bidders ' underlying value distribution. In contrast to the results of previous studies, the researcher does not need to know the number of potential bidders willing to pay the reserve price nor assume that the number of potential bidders is fixed across auctions. I then propose a consistent estimator using the semi-nonparametric maximum likelihood estimation method developed by Gallant and his coauthors. Several Monte Carlo experiments are conducted to illustrate its performance. The simulation results show that the proposed estimator performs well. I apply the proposed method to university yearbook sales on eBay. Using my estimate of bidders ' value distribution, I explore the effects of sellers ' ratings on bidders ' value distribution; compute consumers' surplus; and examine a regularity assumption that is often made in the mechanism design literature.

Abstract — We design truthful double spectrum auctions where multiple parties can trade spectrum based on their individual needs. Open, market-based spectrum trading motivates existing spectrum owners (as sellers) to lease their selected idle spectrum to new spectrum users, and provides new users (as buyers) the spectrum they desperately need. The most significant challenge is how to make the auction economic-robust (truthful in particular) while enabling spectrum reuse to improve spectrum utilization. Unfortunately, existing designs either do not consider spectrum reuse or become untruthful when applied to double spectrum auctions. We address this challenge by proposing TRUST, a general framework for truthful double spectrum auctions. TRUST takes as input any reusability-driven spectrum allocation algorithm, and applies a novel winner determination and pricing mechanism to achieve truthfulness and other economic properties while significantly improving spectrum utilization. To our best knowledge, TRUST is the first solution for truthful double spectrum auctions that enable spectrum reuse. Our results show that economic factors introduce a tradeoff between spectrum efficiency and economic robustness. TRUST makes an important contribution on enabling spectrum reuse to minimize such tradeoff. I.

Algorithmic pricing is the computational problem that sellers (e.g., in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami et al. [9] propose this problem and give logarithmic approximations (in the number of consumers) for the unit-demand and single-parameter cases where there is a specific set of consumers and their valuations for bundles are known precisely. Subsequently several versions of the problem have been shown to have poly-logarithmic inapproximability. This problem has direct ties to the important open question of better understanding the Bayesian optimal mechanism in multi-parameter agent settings; however, for this purpose approximation factors logarithmic in the number of agents are inadequate. It is therefore of vital interest to consider special cases where constant approximations are possible. We consider the unit-demand variant of this pricing problem. Here a consumer has a valuation for each different item and their value for a set of items is simply the maximum value they have for any item in the set. Instead of considering a set of consumers with precisely known preferences, like the prior algorithmic pricing literature, we assume that the preferences of the consumers are drawn from a distribution. This is the standard assumption in economics; furthermore, the setting of a specific set of customers with specific preferences, which is employed in all of the prior work in algorithmic pricing, is a special case of this general Bayesian pricing problem, where there is a discrete Bayesian distribution for preferences specified by picking one consumer uniformly from the given set of consumers. Notice that the distribution over the valuations for the individual items that this generates is obviously correlated. Our work complements these existing works by considering the case where the consumer’s valuations for the different items are independent random variables. Our main

In a market-based scheduling mechanism, the allocation of time-specific resources to tasks is governed by a competitive bidding process. Agents bidding for multiple, separately allocated time slots face the risk that they will succeed in obtaining only part of their requirement, incurring expenses for potentially worthless slots. We investigate the use of price prediction strategies to manage such risk. Given an uncertain price forecast, agents follow simple rules for choosing whether and on which time slots to bid. We find that employing price predictions can indeed improve performance over a straightforward baseline in some settings. Using an empirical game-theoretic methodology, we establish Nash equilibrium profiles for restricted strategy sets. This allows us to confirm the stability of price-predicting strategies, and measure overall efficiency. We further experiment with variant strategies to analyze the source of prediction’s power, demonstrate the existence of self-confirming predictions, and compare the performance of alternative prediction methods.

We address the problem of buying an inexpensive path in a graph in which edges are owned by selfish agents. We show that it is possible to lower the expected payments of the VCG mechanism by deleting a subset of edges of the underlying graph; however, it is NP-hard to determine what is the best subset of edges to delete, or even whether a given graph can benefit from edge deletion.