Abstract This paper considers algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. Such scenarios arise, in particular, when computers or users aim to cooperate or trade over the Internet. 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. This exposition presents a model to formally study such algorithms. This model, based on the field of mechanism design, is taken from the author's joint work with Amir Ronen, and is similar to approaches taken in the distributed AI community in recent years. Using this model, we demonstrate how some of the techniques of mechanism design can be applied towards distributed computation problems. We then exhibit some issues that arise in distributed computation which require going beyond the existing theory of mechanism design. 1 Introduction A large part of research in computer science is concerned with protocols and algorithms for inter-connected collections of computers. The designer of such an algorithm or protocol always makes an implicit assumption that the participating computers will act as instructed- except, perhaps, for the faulty or malicious ones.

In this paper, we present an adaptive organizational policy for multi-agent systems called trace. trace allows a collection of multi-agent organizations to dynamically allocate tasks and resources between themselves in order to eciently process an incoming stream of task requests. trace is intended to cope with environments in which tasks have time constraints, and environments that are subject to load variations. trace is made up of two key elements: the task allocation protocol (tap) and the resource allocation protocol (rap). The tap allows agents to cooperatively allocate their tasks to other agents with the capability and opportunity to successfully carry them out. As requests arrive arbitrarily, at any instant, some organizations could have surplus resources while others could become overloaded. In order to minimize the number of lost requests caused by an overload, the allocation of resources to organizations is changed dynamically by the resource allocation protocol (rap), which uses ideas from computational market systems to allocate resources (in the form of problem solving agents) to organizations. We begin by formally dening the task allocation problem, and show that it is np-complete, and hence that centralized solutions to the problem are unlikely to be feasible. We then introduce the task and resource allocation protocols, focussing on the way in which resources are allocated by the rap. We then present some experimental results, which show that trace exhibits high performance despite unanticipated changes in the environment.

We analyze economic efficiency and equilibrium properties in decentralized task allocation problems involving hierarchical dependencies and resource contention. We bound the inefficiency of a type of approximate equilibrium in proportion to the number of agents and the bidding parameters in a particular market protocol. This protocol converges to an approximate equilibrium with respect to all agents, except those which may acquire unneeded inputs. We introduce a decommitment phase to allow such agents to decommit from their input contracts. Experiments indicate that the augmented market protocol produces highly efficient allocations on average. 1

In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged guarantees efficient global behavior across the supply chain. Each market form a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.

We discuss the design of a trading-agent competition, to be held in conjunction with ICMAS-00. This design will be revised based on deliberations of a committee comprising active researchers in the fieldJ This AmEC-99 presentation constitutes an official announcement of the competition. We solicit feedback from the community regarding further operational and design details, and are hopeful that AmEC attendees will consider participating in the competition.

Eliciting truthful responses from self-interested agents is an important problem in game theory and microeconomics, and it is studied under mechanism design or implementation theory. Truthful mechanisms have received considerable interest within computer science recently for designing protocols for Internet-based applications, which typically involve cooperation of multiple selfinterested agents. A cornerstone of the mechanism design field is the Vickrey mechanism, or more generally the class of Vickrey-Clarke-Groves mechanisms. These mechanisms are known to be incentive-compatible, meaning that rational agents maximize their utility by truthfully revealing their preferences. In the Vickrey-Clarke-Groves (VCG) mechanism, each agent receives a "payment" for his participation, and this payment is proportional to the added "value" he brings to the system. Implementing the VCG mechanism often requires solving a (non-trivial) optimization problem n + 1 times, once with all agents, and once corresponding to each agent's deletion to determine his incremental value. An important algorithmic challenge is to reduce this computational overhead.

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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Supply chain formation is an important problem in the commercial world, and can be improved by greater automated support. Hence, the multiagent systems community should work to develop new solutions to the problem. The problem is complex and challenging, and a complete model must encompass a number of issues. In this paper we highlight some issues that must be understood to make progress in modeling supply chain formation.

In order to effectively respond to changing market conditions, business partners must be able to rapidly form supply chains. This thesis approaches the problem of automating supply chain formation—the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges—within an economic framework. In this thesis, supply chain formation is formalized as task dependency networks. This model captures subtask decomposition in the presence of resource contention—two important and challenging aspects of supply chain formation. In order to form supply chains in a decentralized fashion, price systems provide an economic framework for guiding the decisions of self-interested agents. In competitive price equilibrium, agents choose optimal allocations with respect to prices, and outcomes are optimal overall. Approximate competitive equilibria yield approximately optimal allocations. Different market protocols are proposed for agents to negotiate the allocation of resources to form supply chains. In the presence of resource contention, these protocols produce better solutions than the greedy protocols common in the artificial intelligence