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Strategy-proof Allocation of Multiple Items between Two Agents without Payments or Priors
, 2010
"... We investigate the problem of allocating items (private goods) among competing agents in a setting that is both prior-free and paymentfree. Specifically, we focus on allocating multiple heterogeneous items between two agents with additive valuation functions. Our objective is to design strategy-proo ..."
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We investigate the problem of allocating items (private goods) among competing agents in a setting that is both prior-free and paymentfree. Specifically, we focus on allocating multiple heterogeneous items between two agents with additive valuation functions. Our objective is to design strategy-proof mechanisms that are competitive against the most efficient (first-best) allocation. We introduce the family of linear increasing-price (LIP) mechanisms. The LIP mechanisms are strategy-proof, prior-free, and payment-free, and they are exactly the increasing-price mechanisms satisfying a strong responsiveness property. We show how to solve for competitive mechanisms within the LIP family. For the case of two items, we find a LIP mechanism whose competitive ratio is near optimal (the achieved competitive ratio is 0.828, while any strategy-proof mechanism is at most 0.841-competitive). As the number of items goes to infinity, we prove a negative result that any increasing-price mechanism (linear or nonlinear) has a maximal competitive ratio of 0.5. Our results imply that in some cases, it is possible to design good allocation mechanisms without payments and without priors.
Contract auctions for sponsored search. In
, 2009
"... Abstract. In sponsored search auctions advertisers typically pay a fixed amount per click that their advertisements receive. In particular, the advertiser and the publisher enter into a contract (e.g., the publisher displays the ad; the advertiser pays the publisher 10 cents per click), and each pa ..."
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Cited by 7 (2 self)
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Abstract. In sponsored search auctions advertisers typically pay a fixed amount per click that their advertisements receive. In particular, the advertiser and the publisher enter into a contract (e.g., the publisher displays the ad; the advertiser pays the publisher 10 cents per click), and each party's subjective value for such a contract depends on their estimated click-through rates (CTR) for the ad. Starting from this motivating example, we define and analyze a class of contract auctions that generalize the classical second price auction. As an application, we introduce impression-plus-click pricing for sponsored search, in which advertisers pay a fixed amount per impression plus an additional amount if their ad is clicked. Of note, when the advertiser's estimated CTR is higher than the publisher's estimated CTR, both parties find negative click payments advantageous, where the advertiser pays the publisher a premium for the impression but the publisher then pays the advertiser per click.
Strategyproof Classification
, 2011
"... Experts reporting the labels used by a learning algorithm cannot always be assumed to be truthful. We describe recent advances in the design and analysis of strategyproof mechanisms for binary classification, and their relation to other mechanism design problems. ..."
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Cited by 3 (2 self)
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Experts reporting the labels used by a learning algorithm cannot always be assumed to be truthful. We describe recent advances in the design and analysis of strategyproof mechanisms for binary classification, and their relation to other mechanism design problems.
Strategy-Proof Contract Auctions and the Role of Ties ✩
"... A contract auction establishes a contract between a center and one of the bidders. As contracts may describe many terms, preferences over contracts typically display indifferences. The Qualitative Vickrey Auction (QVA) selects the best contract for the winner that is at least as good for the center ..."
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A contract auction establishes a contract between a center and one of the bidders. As contracts may describe many terms, preferences over contracts typically display indifferences. The Qualitative Vickrey Auction (QVA) selects the best contract for the winner that is at least as good for the center as any of the contracts offered by the non-winning players. When each bidder can always offer a contract with higher utility for the center at an arbitrarily small loss of her own utility, the QVA is the only mechanism that is individually rational, strategy-proof, selects stable outcomes, and is Pareto-efficient. For general continuous utility functions, a variant of the QVA involving fixed tie-breaking is strategy-proof and also selects stable outcomes. However, there is no mechanism in this setting that in addition also selects Pareto-efficient outcomes.
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"... Internet search companies generate the majority of their multibillion dollar revenue from selling ad placement, often in the form of short, textual ads that appear next to web search results. Such sponsored search ads are typically sold via pay-perclick auctions in which advertisers bid for placemen ..."
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Internet search companies generate the majority of their multibillion dollar revenue from selling ad placement, often in the form of short, textual ads that appear next to web search results. Such sponsored search ads are typically sold via pay-perclick auctions in which advertisers bid for placement, but pay publishers (i.e., search
and
"... Internet search companies generate the majority of their multibillion dollar revenue from selling ad placement, often in the form of short, textual ads that appear next to web search results. Such sponsored search ads are typically sold via pay-perclick auctions in which advertisers bid for placemen ..."
Abstract
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Internet search companies generate the majority of their multibillion dollar revenue from selling ad placement, often in the form of short, textual ads that appear next to web search results. Such sponsored search ads are typically sold via pay-perclick auctions in which advertisers bid for placement, but pay publishers (i.e., search
Approximating the Qualitative Vickrey Auction by a Negotiation Protocol
"... Abstract. A result of Bulow and Klemperer has suggested that auc-tions may be a better tool to obtain an efficient outcome than negotia-tion. For example, some auction mechanisms can be shown to be efficient and strategy-proof. However, they generally also require that additional constraints are met ..."
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Abstract. A result of Bulow and Klemperer has suggested that auc-tions may be a better tool to obtain an efficient outcome than negotia-tion. For example, some auction mechanisms can be shown to be efficient and strategy-proof. However, they generally also require that additional constraints are met which are not always easy to guarantee in practice. It thus is interesting to find methods that do not impose such constraints but still approximate the theoretically predicted outcome of the mech-anism. In this paper we show that a negotiation protocol may be used to this end if the negotiating agents are capable of learning opponent preferences. The latter condition can be met by current state of the art negotiation technology. We present a protocol that approximates the theoretical outcome predicted by a so-called Qualitative Vickrey auction mechanism. 1
Approximating an Auction Mechanism by Multi-Issue Negotiation
"... The main question addressed in this paper is whether a theoretical outcome determined by an auction mechanism can be reasonably approximated by negotiation among agents in order to drop some of the unrealistic constraints or assumptions presupposed by the mechanism. In particular, we are interested ..."
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The main question addressed in this paper is whether a theoretical outcome determined by an auction mechanism can be reasonably approximated by negotiation among agents in order to drop some of the unrealistic constraints or assumptions presupposed by the mechanism. In particular, we are interested in whether the assumption that a buyer publicly announces her preferences in order to guarantee perfect knowledge of these preferences can be dropped if a negotiating agent is used that can learn preferences. We show how to setup a multiplayer multi-issue negotiation process where preferences are learned, and we investigate how the results of this process relate to the theoretical result of holding an auction in the case of complete knowledge about the preferences of the buyer. Experiments show that the outcomes obtained by negotiating agents that learn opponent preferences approximate the outcome predicted by the mechanism. It thus follows that the assumption of perfect knowledge about buyer preferences can be removed when players are equipped with proper learning capabilities. We also investigate whether the procedure dictated by the mechanism can be further relaxed but in that case experiments indicate that more complex considerations about the market need to be taken into account. 1.
Computationally Feasible Approaches to . . .
, 2010
"... In many multiagent settings, a decision must be made based on the preferences of multiple agents, and agents may lie about their preferences if this is to their benefit. In mechanism design, the goal is to design procedures (mechanisms) for making the decision that work in spite of such strategic be ..."
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In many multiagent settings, a decision must be made based on the preferences of multiple agents, and agents may lie about their preferences if this is to their benefit. In mechanism design, the goal is to design procedures (mechanisms) for making the decision that work in spite of such strategic behavior, usually by making untruthful behavior suboptimal. In automated mechanism design, the idea is to computationally search through the space of feasible mechanisms, rather than to design them analytically by hand. Unfortunately, the most straightforward approach to automated mechanism design does not scale to large instances, because it requires searching over a very large space of possible functions. In this dissertation, we adopt an approach to automated mechanism design that is computationally feasible. Instead of optimizing over all feasible mechanisms, we carefully choose a parameterized subfamily of mechanisms. Then we optimize over mechanisms within this family. Finally, we analyze whether and to what extent the resulting mechanism is suboptimal outside the subfamily. We apply (computationally feasible) automated mechanism design to three resource allocation mechanism design problems: mechanisms that redistribute
