Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of trade-offs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the trade-off strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make trade-offs for multi-dimensional goods for the problem of distributed resource allocation.

The allocation of discrete, complementm'y resources is a fundamental probleln in econolnics and of direct interest to e-colnlnerce applications. Combinatorial auctions account for complementarities by optimizing over offers expressed in terms of bundles. Progressive versions of combinatorial auctions alleviate the burden on bidders of expressing offers for all bundles of interest by providing interiln feedback based on partial sets of bids. Feedback in terms of hypothetical prices is particularly useful, as it directs bidders to,yard those bundles potentially yielding the greatest surplus. For a general class of discrete resource allocation problelns vith free disposal, we establish by construction the existence of competitive equilibriuln prices on bundles that support the efficient allocation. We introduce AkBA, a falnily of progressive auctions that use these equilibriuln bundle prices. We exalnine a particular instance of the family, called A1BA, and present SOlne elnpirical data on its perforlnance.

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.

We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms perform a polynomial number of queries. We also give conditions under which the resulting algorithms have polynomial communication. Our conversion procedure allows us to generate combinatorial auction protocols from learning algorithms for polynomials, monotone DNF, and linear-threshold functions. In particular, we obtain an algorithm that elicits XOR bids with polynomial communication. We then characterize the communication requirements of implementing Vickrey payments with an elicitation algorithm. This suggests a modification to the queries in our elicitation algorithms so that truthful bidding becomes an ex-post Nash equilibrium.

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Supply chain formation presents difficult coordination issues for distributed negotiation protocols. Agents must simultaneously negotiate production relationships at multiple levels, with important interdependencies among inputs and outputs at each level. Combinatorial auctions address this problem by global optimization over expressed offers to engage in compound exchanges. A one-shot combinatorial auction that optimizes the reported value of the bids results in optimal allocations with truthful bids. But autonomous self-interested agents have an incentive to bid strategically in an attempt to gain extra surplus. We investigate a particular combinatorial protocol consisting of a one-shot auction and a strategic bidding policy. We experimentally analyze the efficiency and producer surplus obtained in five networks, and compare this performance to that of a distributed, progressive auction protocol with non-strategic bidding. We find that producers can sometimes gain significantly by bi...

This paper analyzes individually-rational ex post equilibrium in the VC (Vickrey-Clarke) combinatorial auctions. If \Sigma is a family of bundles of goods, the organizer may restrict the participants by requiring them to submit their bids only for bundles in \Sigma. The \Sigma-VC combinatorial auctions (multi-good auctions) obtained in this way are known to be individually-rational truthtelling mechanisms. In contrast, this paper deals with non-restricted VC auctions, in which the buyers restrict themselves to bids on bundles in \Sigma, because it is rational for them to do so. That is, it may be that when the buyers report their valuation of the bundles in \Sigma, they are in an equilibrium. We fully characterize those \Sigma that induce individually rational equilibrium in every VC auction, and we refer to the associated equilibrium as a bundling equilibrium. The number of bundles in \Sigma represents the communication complexity of the equilibrium. A special case of bundling equilibrium is partition-based equilibrium, in which \Sigma is a field, that is, it is generated by a partition. We analyze the tradeoff between communication complexity and economic efficiency of bundling equilibrium, focusing in particular on partition-based equilibrium.

In this chapter, we describe the use of economic principles as the basis for Grid resource allocation policies and mechanisms. A computational economy in which users “buy ” resources from their owners is an attractive method of controlling Grid resource allocation for several reasons. Economies are intuitively easy to understand, they fit the model of flexible resource usage under local control (which is fundamental to Grid computing), and they can be analyzed through a considerable body of extant theory. We discuss many of the fundamental characteristics of computational economies, particularly as they pertain to Grid computing. We also present G-commerce — a framework that we have used to investigate Grid resource economies — as an example of the type of results that are possible. Finally, we discuss several of the issues associated with empirical investigation of Grid economies as a motivation for future work. 1

We consider a manufacturer who uses a reverse, or procurement, auction to determine which supplier will be awarded a contract. Each bid consists of a price and a set of non-price attributes (e.g., quality, lead time). The manufacturer is assumed to know the parametric form of the suppliers’cost functions (in terms of the non-price attributes), but has no prior information on the parameter values. We construct a multi-round open-ascending auction mechanism, where the manufacturer announces a slightly different scoring rule (i.e., a function that ranks the bids in terms of the price and non-price attributes) in each round. Via inverse optimization, the manufacturer uses the bids from the first several rounds to learn the suppliers’cost functions, and then in the final round chooses a scoring rule that attempts to maximize his own utility. Under the assumption that suppliers submit their myopic best-response bids in the last round, and do not distort their bids in the earlier rounds (i.e., they choose their minimum-cost bid to achieve any given score), our mechanism indeed maximizes the manufacturer’s utility within the open-ascending format. We also discuss several enhancements that improve the robustness of our mechanism with respect to the model’s informational and behavioral assumptions. December