We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents ’ interests are best served by behaving correctly. Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. Our main technical contribution concerns the study of a representative task scheduling problem for which the standard mechanism design tools do not suffice. Journal of Economic Literature

We investigate cost-sharing algorithms for multicast transmission. Economic considerations point to two distinct mechanisms, marginal cost and Shapley value, as the two solutions most appropriate in this context. We prove that the former has a natural algorithm that uses only two messages per link of the multicast tree, while we give evidence that the latter requires a quadratic total number of messages. We also show that the welfare value achieved by an optimal multicast tree is NP-hard to approximate within any constant factor, even for bounded-degree networks. The lower-bound proof for the Shapley value uses a novel algebraic technique for bounding from below the number of messages exchanged in a distributed computation; this technique may prove useful in other contexts as well. 1

Abstract. Some important classical mechanisms considered in Microeconomics and Game Theory require the solution of a difficult optimization problem. This is true of mechanisms for combinatorial auctions, which have in recent years assumed practical importance, and in particular of the gold standard for combinatorial auctions, the Generalized Vickrey Auction (GVA). Traditional analysis of these mechanisms—in particular, their truth revelation properties—assumes that the optimization problems are solved precisely. In reality, these optimization problems can usually be solved only in an approximate fashion. We investigate the impact on such mechanisms of replacing exact solutions by approximate ones. Specifically, we look at a particular greedy optimization method. We show that the GVA payment scheme does not provide for a truth revealing mechanism. We introduce another scheme that does guarantee truthfulness for a restricted class of players. We demonstrate the latter property by identifying natural properties for combinatorial auctions and showing that, for our restricted class of players, they imply that truthful strategies are dominant. Those properties have applicability beyond the specific auction studied.

Selfish hosts in wireless networks that fail to adhere to the MAC protocol may obtain an unfair share of the channel bandwidth. We present modifications to the IEEE 802.11 backoff mechanism to simplify detection of such selfish hosts. We also present a correction scheme for penalizing greedy misbehavior which attempts to restrict the misbehaving nodes to a fair share of the channel bandwidth. Simulation results indicate that our detection and correction schemes are fairly successful in handling MAC layer misbehavior.

This paper deals with multi-unit combinatorial auctions where there are n types of goods for sale, and for each good there is some fixed number of units. We focus on the case where each bidder desires a relatively small number of units of each good. In particular, this includes the case where each good has exactly k units, and each bidder desires no more than a single unit of each good. We provide incentive compatible mechanisms for combinatorial auctions for the general case where bidders are not limited to single minded valuations. The mechanisms we give have approximation ratios close to the best possible for both on-line and off-line scenarios. This is the first result where non-VCG mechanisms are derived for non-single minded bidders for a natural model of combinatorial auctions.

Weak monotonicity is a simple necessary condition for a social choice function to be implementable by a truthful mechanism. Roberts [10] showed that it is sufficient for all social choice functions whose domain is unrestricted. Lavi, Mu’alem and Nisan [6] proved the sufficiency of weak monotonicity for functions over order-based domains and Gui, Muller and Vohra [5] proved sufficiency for order-based domains with range constraints and for other special types of linear inequality constraints on the domain. Here we generalize these results by showing that weak monotonicity is sufficient for functions defined on any convex domain. 1

We introduce the notion of coordination mechanisms to improve the performance in systems with independent selfish and non-colluding agents. The quality of a coordination mechanism is measured by its price of anarchy—the worst-case performance of a Nash equilibrium over the (centrally controlled) social optimum. We give upper and lower bounds for the price of anarchy for selfish task allocation and congestion games.

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Conventionally, most network protocols assume that the network entities who participate in the network activities will always behave as instructed. However, in practice, most network entities are selfish: they will try to maximize their own benefits instead of altruistically contributing to the network by following the prescribed protocols. Thus, new protocols should be designed for the non-cooperative network that is composed of selfish entities. In this paper, we specifically show how to design truthful multicast protocols for non-cooperative networks such that these selfish entities will follow the protocols out of their own interests. By assuming that every entity has a fixed cost for a specific multicast, we give a general framework to decide whether it is possible and how, if possible, to transform an existing multicast protocol to a truthful multicast protocol by designing a proper payment protocol. We then show how the payments to those relay entities are shared fairly among all receivers so that it encourages collaboration among receivers. As running examples, we show how to design truthful multicast protocols for several multicast structures that are currently used in practice.