Governing Communities by Auction

Common interest communities have become the property form of choice for many Americans. As of 2010, sixty-two million Americans lived in common interest communities. Residents benefit from sharing the cost of common amenities – pools, lawns, gazebos – and from rules that ensure compliance with community expectations. But decisionmaking in common interest communities raises serious concerns about minority abuse and manipulation, a problem well known to all property law students. Decisions about which amenities will be provided and which rules will be enacted are typically made through some combination of delegation and voting. Delegates often act for their own benefit, and, for a variety of reasons, voting fails to capture the preferences of the community. This Article suggests a better way. Building upon the pioneering work of Vickrey, Clarke, and Groves, we propose a novel auction system that captures the intensity of resident preferences while preserving the honesty of declared preferences. The use of auction theory induces truthful revelation of preferences by participants and reflects the intensity of preference for any given policy outcome. As a result, our system allows communities to make better decisions and makes common interest communities more responsive to the needs of residents

Resilient Mechanisms For Truly Combinatorial Auctions

Dominant-strategy truthfulness is traditionally considered the best possible solution concept in mechanism design, as it enables one to predict with confidence which strategies INDEPENDENT players will actually choose. Yet, as with any other form of equilibrium, it too can be extremely vulnerable to COLLUSION. The problem of collusion is particularly evident for UNRESTRICTED combinatorial auctions}, arguably the hardest type of auctions.We thus investigate how much revenue can be guaranteed, in unrestricted combinatorial auctions, by dominant-strategy-truthful mechanisms that are COLLUSION-RESILIENT in a very strong sense; and obtain almost matching upper- and lower-bounds

Collusion-Resilient Revenue In Combinatorial Auctions

In auctions of a single good, the second-price mechanism achieves, in dominantstrategies, a revenue benchmark that is naturally high and resilient to anypossible collusion.We show how to achieve, to the maximum extent possible, the same propertiesin combinatorial auctions

Auctions and bidding: A guide for computer scientists

There is a veritable menagerie of auctions-single-dimensional, multi-dimensional, single-sided, double-sided, first-price, second-price, English, Dutch, Japanese, sealed-bid-and these have been extensively discussed and analyzed in the economics literature. The main purpose of this article is to survey this literature from a computer science perspective, primarily from the viewpoint of computer scientists who are interested in learning about auction theory, and to provide pointers into the economics literature for those who want a deeper technical understanding. In addition, since auctions are an increasingly important topic in computer science, we also look at work on auctions from the computer science literature. Overall, our aim is to identifying what both these bodies of work these tell us about creating electronic auctions. © 2011 ACM.This work was funded in part by HP under the “Always on” grant, by NSF IIS-0329037 “Tools and Techniques for Automated Mechanism Design”, and by IEA (TIN2006-15662-C02-01), OK (IST-4-027253-STP), eREP(EC-FP6-CIT5-28575) and Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010).Peer Reviewe

Revenue in Truly Combinatorial Auctions and Adversarial Mechanism Design

Little is known about generating revenue in UNRESTRICTED combinatorial auctions. (In particular, the VCG mechanism has no revenue guarantees.) In this paper we determine how much revenue can be guaranteed in such auctions. Our analysis holds both in the standard model, when all players are independent and rational, as well as in a most adversarial model, where some players may bid collusively or even totally irrationally

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Optimal Shill Bidding in the VCG Mechanism

This paper studies shill bidding in the VCG mechanism applied to combinatorial auctions. Shill bidding is a strategy whereby a single decision-maker enters the auction under the guise of multiple identities (Sakurai, Yokoo, and Matsubara 1999). I formulate the problem of optimal shill bidding for a bidder who knows the aggregate bid of her opponents. A key to the analysis is a subproblem--the cost minimization problem (CMP)--which searches for the cheapest way to win a given package using shills. An analysis of the CMP leads to several fundamental results about shill bidding: (i) I provide an exact characterization of the aggregate bids b such that some bidder would have an incentive to shill bid against b in terms of a new property, Submodularity at the Top; (ii) the problem of optimally sponsoring shills is equivalent to the winner determination problem (for single minded bidders)--the problem of finding an efficient allocation in a combinatorial auction; (iii) shill bidding can occur in equilibrium; and (iv) the problem of shill bidding has an inverse, namely the collusive problem that a coalition of bidders may have an incentive to merge (even after competition among coalition members has been suppressed). I show that only when valuations are additive can the incentives to shill and merge simultaneously disappear.VCG mechanism, combinatorial auctions, winner determination problem, collusion.

Payment Rules through Discriminant-Based Classifiers

In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality