Goldberg et al. [6] recently began the study of incentivecompatible auctions for digital goods, that is, goods which are available in unlimited supply. Many digital goods, however, such as books, music, and software, are sold continuously, rather than in a single round, as is the case for traditional auctions. Hence, it is important to consider what happens in the online version of such auctions. We de ne a model for online auctions for digital goods, and within this model, we examine auctions in which bidders have an incentive to bid their true valuations, that is, incentivecompatible auctions. Since the best oine auctions achieve revenue comparable to the revenue of the optimal xed pricing scheme, we use the latter as our benchmark. We show that deterministic auctions perform poorly relative to this benchmark, but we give a randomized auction which is within a factor O(exp( p log log h)) of the benchmark, where h is the ratio between the highest and lowest bids. As part of this result, we also give a new oine auction, which improves upon the previously best auction in a certain class of auctions for digital goods. We also give lower bounds for both randomized and deterministic online auctions for digital goods. 1

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... this paper reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple auctions with varying start and end times and with varying protocols (including English, Dutch and Vickrey). The framework is flexible, configurable, and enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user's preferences. Given this large space of possibilities, we employ a genetic algorithm to search (offline) for effective strategies in common classes of environment. The strategies that emerge from this evolution are then codified into the agent's reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. The proposed framework has been implemented in a simulated marketplace environment and its effectiveness has been empirically demonstrated.

The increasing number of online auctions poses a big challenge to e-consumers, especially to those who are actively looking for good deals. In this paper, we present the design of an autonomous agent that can alleviate some of these problems by participating across multiple online auctions (in particular, English, Dutch, and Vickrey auctions). The agent makes decisions on behalf of the consumer and endeavours to guarantee the delivery of the item according to the user's preferences. Our agent monitors and collects information from the ongoing auctions and determines which auction it wishes to participate in. The decision on how much to bid in the selected auction is made based on a series of tactics and strategies. The proposed bidding algorithm has been implemented in a simulated marketplace environment and its performance has been evaluated empirically. 1

We introduce ecient learning equilibrium (ELE), a normative approach to learning in non-cooperative settings. In ELE, the learning algorithms themselves are required to be in equilibrium. In addition, the learning algorithms must arrive at a desired value after polynomial time, and a deviation from the prescribed ELE become irrational after polynomial time. We prove the existence of an ELE (where the desired value is the expected payoff in a Nash equilibrium) and of a Pareto-ELE (where the objective is the maximization of social surplus) in repeated games with perfect monitoring. We also show that an ELE does not always exist in the imperfect monitoring case. Finally, we discuss the extension of these results to general-sum stochastic games.

We study truthful mechanisms for auctions in which the auctioneer is trying to hire a team of agents to perform a complex task, and paying them for their work. As common in the field of mechanism design, we assume that the agents are selfish and will act in such a way as to maximize their profit, which in particular may include misrepresenting their true incurred cost. Our first contribution is a new and natural definition of the frugality ratio of a mechanism, measuring the amount by which a mechanism “overpays”, and extending previous definitions to all monopoly-free set systems. After reexamining several known results in light of this new definition, we proceed to study in detail shortest path auctions and “r-out-of-k sets ” auctions. We show that when individual set systems (e.g., graphs) are considered instead of worst cases over all instances, these problems exhibit a rich structure, and the performance of mechanisms may be vastly different. In particular, we show that the wellknown VCG mechanism may be far from optimal in these settings, and we propose and analyze a mechanism that is always within a constant factor of optimal. 1

We consider the problem of designing mechanisms with the incentive property that no coalition of agents can engage in a collusive strategy that results in an increase in the combined utility of the coalition. For single parameter agents, we give a characterization that essentially restricts such mechanisms to those that post a “take it or leave it ” price to for each agent in advance. We then consider relaxing the incentive property to only hold with high probability. In this relaxed model, we are able to design approximate profit maximizing auctions and approximately efficient auctions. We also give a general framework for designing mechanisms for single parameter agents while maintaining the coalition incentive property with high probability. In addition, we give several results for a weaker incentive property from the literature known as group strategyproofness.

this paper, we discuss the design of algorithms for agents to use when participating in multiple simultaneous English auctions, aiming to purchase multiple goods. Firstly, we present a coordination algorithm, which ensures the agent places appropriate bids in the different auctions to buy exactly the right number of goods. Secondly, we combine this with an algorithm to determine what maximum bid an agent should place in an auction which is about to terminate. This algorithm combines a belief-based model of the auctions with a utility analysis. This analysis is to trade off the certain outcome of the terminating auction against the possible outcomes of the remaining auctions, and hence place appropriate bids in each

Wedeveloptestsforcommonvaluesatfirst-price sealed-bid auctions. Our tests are nonparametric, require observation only of the bids submitted at each auction, and are based on the fact that the “winner’s curse ” arises only in common values auctions. The tests build on recently developed methods for using observed bids to estimate each bidder’s conditional expectation of the value of winning the auction. Equilibrium behavior implies that in a private values auction these expectations are invariant to the number of opponents each bidder faces, while with common values they are decreasing in the number of opponents. This distinction forms the basis of our tests. We consider both exogenous and endogenous variation in the number of bidders. Monte Carlo experiments show that our tests can perform well in samples of moderate sizes. We apply our tests to two different types of U.S. Forest Service timber auctions. For unit-price (“scaled”) sales often argued to fit a private values model, our tests consistently fail to find evidence of common values. For “lumpsum ” sales, where aprioriarguments for common values appear stronger, our tests yield mixed evidence against the private values hypothesis.

Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus.