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

One of the major achievements of mechanism design theory is the family of truthful (incentive compatible) mechanisms often called VCG (named after Vickrey, Clarke and Groves). When applying VCG mechanisms to complex mechanism design problems such as combinatorial auctions a problem emerges: even finding optimal outcomes is computationally intractable. A striking observation is that if the optimal outcome is replaced by the results of computationally tractable approximation algorithms or heuristics then the resulting mechanism (termed VCG-based) is no longer necessarily truthful! The first part of this paper considers this problem in depth and shows that it is almost universal. Specifically, we prove that essentially all reasonable approximations or heuristics for combinatorial auctions as well as a wide class of cost minimization problems yield non-truthful VCG-based mechanisms. The second part of this paper proposes a method for handling this non-truthfulness. We introduce a...

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We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacy [15, 14], in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacy-preserving auction mechanism, we introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal fixed price, and a slight change in the offered price may take the revenue from optimal to zero. 1

Iterative auctions have many computational advantages over sealed-bid auctions, but can present new possibilities for strategic manipulation. We propose a two-stage technique to make iterative auctions that compute optimal allocations with myopic best-response bidding strategies more robust to manipulation. First, introduce proxy bidding agents to constrain bidding strategies to (possibly untruthful) myopic best-response. Second, after...

As traditional commerce moves on-line more business transactions will be mediated by software agents, and the ability of agent-mediated electronic marketplaces to efficiently allocate resources will be highly dependent on the complexity of the decision problems that agents face; determined in part by the structure of the marketplace, resource characteristics, and the nature of agents' local problems. We compare auction performance for agents that have hard local problems, and uncertain values for goods. Perhaps an agentmust solve a hard optimization problem to value a good, or interact with a busy and expensivehuman expert. Although auction design cannot simplify the valuation problem itself, we show that good auction design can simplify meta-deliberation -- providing incentives for the "right" agents to deliberate for the "right" amount of time. Empirical results for a particular cost-benefit model of deliberation show that an ascending-price auction will often support higher revenue and efficiency than other auction designs. The price provides agents with useful information about the value that other agents hold for the good.

Using market mechanisms for resource allocation in distributed systems is not a new idea, nor is it one that has caught on in practice or with a large body of computer science research. Yet, projects that use markets for distributed resource allocation recur every few years [1, 2, 3], and a new generation of research is exploring market-based resource allocation mechanisms [4, 5, 6, 7, 8] for distributed environments such as Planetlab, Netbed, and computational grids.

Many recent applications of interest involve self-interested participants. As such participants, termed agents, may manipulate the algorithm for their own benefit, a new challenge emerges: The design of algorithms and protocols that perform well when the agents behave according to their own self-interest. This led several researchers to consider computational models that are based on a sub-field of game-theory and micro-economics called mechanism design. This paper introduces this topic mainly through examples. It demonstrates that in many cases selfishness can be satisfactorily overcome, surveys some of the recent trends in this area and presents new challenging problems. The paper is mostly based on classic results from mechanism design as well as on recent work by the author and others.

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