Distributed Algorithmic Mechanism Design (DAMD) combines theoretical computer science’s traditional focus on computational tractability with its more recent interest in incentive compatibility and distributed computing. The Internet’s decentralized nature, in which distributed computation and autonomous agents prevail, makes DAMD a very natural approach for many Internet problems. This paper first outlines the basics of DAMD and then reviews previous DAMD results on multicast cost sharing and interdomain routing. The remainder of the paper describes several promising research directions and poses some specific open problems.

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We analyze the core of a class of coalition formation game in which every player's payoff depends only on the members of her coalition. We first consider anonymous games and additively separable games. Neither of these strong properties guarantee the existence of a core allocation, even if additional strong properties are imposed. We then introduce two top-coalition properties each of which guarantee the existence. We show that these properties are independent of the Scarf-balancedness condition. Finally we give several economic applications.

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Most models for online advertising assume that an advertiser’s value from winning an ad auction, which depends on the clickthrough rate or conversion rate of the advertisement, is independent of other advertisements served alongside it in the same session. This ignores an important externality effect: as the advertising audience has a limited attention span, a high-quality ad on a page can detract attention from other ads on the same page. That is, the utility to a winner in such an auction also depends on the set of other winners. In this paper, we introduce the problem of modeling externalities in online advertising, and study the winner determination problem in these models. Our models are based on choice models on the audience side. We show that in the most general case, the winner determination problem is hard even to approximate. However, we give an approximation algorithm for this problem with an approximation factor that is logarithmic in the ratio of the maximum to the minimum bid. Furthermore, we show that there are some interesting special cases, such as the case where the audience preferences are single peaked, where the problem can be solved exactly in polynomial time. For all these algorithms, we prove that the winner determination algorithm can be combined with VCG-style payments to yield truthful mechanisms.

We consider a broad class of situations where a society must choose from a finite set of alternatives. This class includes, as polar cases, those where the preferences of agents are completely unrestricted and those where their preferences are single-peaked. We prove that strategy-proof mechanisms in all these domains must be based on a generalization of the median voter principle. Moreover, they must satisfy a property, to be called the "intersection property," which becomes increasingly stringent as the preference domain is enlarged. In most applications, our results precipitate impossibility theorems. In particular, they imply the Gibbard Satterthwaite theorem as a corollary.

Strategy-proofness, requiring that truth-telling be a dominant strategy, is a standard concept in social choice theory. However, this concept has serious drawbacks. In particular, many strategy-proof mechanisms have multiple Nash equilibria, some of which produce the wrong outcome. A possible solution to this problem is to require double implementation in Nash equilibrium and in dominant strategies, i.e., secure implementation. We characterize securely implementable social choice functions and investigate the connections with dominant strategy implementation and robust implementation. We show that in standard quasi-linear environments with divisible private or public goods, there exist surplus-maximizing (non-dictatorial) social choice functions that can be securely implemented.

Axiomatic Cost and Surplus-Sharing The equitable division of a joint cost (or a jointly produced output) among agents with different shares or types of output (or input) commodities, is a central theme of the theory of cooperative games with transferable utility. Ever since Shapley’s seminal contribution in 1953, this question has generated some of the deepest axiomatic results of modern microeconomic theory. More recently, the simpler problem of rationing a single commodity according to a profile of claims (reflecting individual needs, or demands, or liabilities) has been another fertile ground for axiomatic analysis. This rationing model is often called the bankruptcy problem in the literature. This Chapter reviews the normative literature on these two models, and emphasizes their deep structural link via the Additivity axiom for cost sharing: individual cost shares depend additively upon the cost function. Loosely speaking, an additive cost sharing method can be written as the integral of a rationing method, and this representation defines a linear isomorphism between additive cost sharing methods and rationing methods.

Abstract We consider the problem of locating facilities in a metric space to serve a set of selfish agents. The cost of an agent is the distance between her own location and the nearest facility. The social cost is the total cost of the agents. We are interested in designing strategy-proof mechanisms without payment that have a small approximation ratio for social cost. A mechanism is a (possibly randomized) algorithm which maps the locations reported by the agents to the locations of the facilities. A mechanism is strategy-proof if no agent can benefit from misreporting her location in any configuration. This setting was first studied by Procaccia and Tennenholtz We first prove an Ω(n) lower bound of the social cost approximation ratio for deterministic strategyproof mechanisms. Our lower bound even holds for the line metric space. This significantly improves the previous constant lower bounds

We address the problem of learning and implementation in the Internet. When agents play repeated games in distributed environments like the Internet, they have very limited a priori information about the other players and the payo matrix. Consequently, standard solution concepts like Nash equilibria, or even the serially undominated set, do not apply in such a setting. To construct more appropriate solution concepts, we rst describe the essential properties that constitute \reasonable " learning behavior in distributed environments. We then study the convergence behavior of such algorithms; these results lead us to propose rather non traditional solutions concepts for this context. Finally, we discuss implementation of social choice functions with these solution concepts, and nd that only strictly coalitionally strategyproof social choice functions are implementable. 1 1