We envision a future in which the global economy and the Internet will merge and evolve together into an information economy bustling with billions of economically motivated software agents that exchange information goods and services with humans and other agents. Economic software agents will differ in important ways from their human counterparts, and these differences may have significant beneficial or harmful effects upon the global economy. It is therefore important to consider the economic incentives and behaviors of economic software agents, and to use every available means to anticipate their collective interactions. We survey research conducted by the Information Economies group at IBM Research aimed at understanding collective interactions among agents that dynamically price information goods or services. In particular, we study the potential impact of widespread shopbot usage on prices, the price dynamics that may ensue from various mixtures of automated pricing ...

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.

The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of emarketplaces and to motivate researchers to apply unique approaches to a common task. This article

We consider auction design in a setting with costly preference elicitation. We motivate the role of proxy agents, that are situated between bidders and the auction, and maintain partial information about agent preferences and compute equilibrium bidding strategies based on the available information. The proxy agents can also elicit additional preference information incrementally during an auction. We show that indirect mechanisms, such as proxied ascending-price auctions, can achieve better allocative efficiency with less preference elicitation than direct mechanisms, such as sealed-bid auctions.

. Shopbots are agents that search the Internet for information pertaining to the price and quality of goods or services. With the advent of shopbots, a dramatic reduction in search costs is imminent, which promises (or threatens) to radically alter market behavior. This research includes the proposal and theoretical analysis of a simple economic model which is intended to capture some of the essence of shopbots, and attempts to shed light on their potential impact on markets. Moreover, experimental simulations of an economy of software agents are described, which are designed to model the dynamic interaction of electronic buyers, sellers, and shopbots. This study forms part of a larger research program that aims to provide new insights on the impact of agent and information technology on the nascent information economy. 1 Introduction Shopbots, agents that automatically search the Internet for goods and services on behalf of consumers, herald a future in which autonomous agents become...

Shopbots are software agents that automatically query multiple sellers on the Internet to gather information about prices and other attributes of consumer goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers minimize expenditure and maximize satisfaction. In response at least partly to this trend, it is anticipated that sellers will come to rely on pricebots, automated agents that employ price-setting algorithms in an attempt to maximize profits. This paper reaches toward an understanding of strategic pricebot dynamics. More specifically, this paper is a comparative study of four candidate price-setting strategies that differ in informational and computational requirements: gametheoretic pricing (GT), myoptimal pricing (MY), derivative following (DF), and Q-learning (Q). In an effort to gain insights into the tradeoffs between practicality and pro tability of pricebot algorithms, the dynamic behavior that arises among homogeneous and heterogeneous collections of pricebots and shopbot-assisted buyers is analyzed and simulated. In homogeneous settings -- when all pricebots use the same pricing algorithm -- DFs outperform MYs and GTs. Investigation of heterogeneous collections of pricebots, however, reveals an incentive for individual DFs to deviate to MY or GT. The Q strategy exhibits superior performance to all the others since it learns to predict and account for the long-term consequences of its actions. Although the current implementation of Q is impractically expensive, techniques for achieving similar performance at greatly reduced computational cost are under investigation.

increasingly important aspect of e-commerce, both in the business to business and business to consumer domains. As a result of this, it is often possible to find many auctions selling similar goods on the web. However, when an individual is attempting to purchase such a good, they will usually bid in one, or a small number, of such auctions. This results in two forms of ine#ciency. Firstly, the individual may pay more for the good than would be expected in an ideal market. Secondly, some sellers may fail to make a sale that could take place in an ideal market.

This paper addresses the question what is the outcome of multi-agent learning via no-regret algorithms in repeated games? Specically, can the outcome of no-regret learning be characterized by traditional game-theoretic solution concepts, such as Nash equilibrium ? The conclusion of this study is that no-regret learning is reminiscent of ctitious play: play converges to Nash equilibrium in dominancesolvable, constant-sum, and generalsum 2 2 games, but cycles exponentially in the Shapley game. Notably, however, the information required of ctitious play far exceeds that of noregret learning. 1.

Abstract. We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specifically the United States FCC wireless frequency spectrum auctions. In addition to the complexity of these auctions, which provides ample opportunities for intelligent approaches to bidding, this type of auction has huge commercial importance, each bringing in billions of dollars to governments around the world. We implement straightforward sample agents in FAucS and use them to replicate known beneficial bidding strategies in this type of auction. We then discuss potential in-depth studies of autonomous bidding agent behaviors using FAucS. The main contribution of this work is the implementation, description, and empirical validation of the FAucS testbed. We present it as a challenging and promising AI research domain. 1

Service composition is the act of taking several component products or services, and bundling them together to meet the needs of a given customer. In the future, service composition will play an increasingly important role in ecommerce, and automation will be desirable to improve speed and efficiency of customer response. In this paper, we discuss the technical issues surrounding the automation of dynamic electronic service composition, using a ficticious company, freighmixer, to demonstrate the process. We focus specifically on the issue of appropriate negotiation strategies for service composition, and present the specification of an algorithm to provide a robust solution to these problems in the context of multiple simultaneous auctions. We present a worked example to demonstrate the behaviour of the algorithm, and discuss related and future work.