This paper provides an elementary, non-technical, survey of auction theory, by introducing and describing some of the critical papers in the subject. (The most important of these are reproduced in a companion book, The Economic Theory of Auctions, Paul Klemperer (ed.), Edward Elgar (pub.), forthcoming.) We begin with the most fundamental concepts, and then introduce the basic analysis of optimal auctions, the revenue equivalence theorem, and marginal revenues. Subsequent sections address risk-aversion, affiliation, asymmetries, entry, collusion, multi-unit auctions, double auctions, royalties, incentive contracts, and other topics. Appendices contain technical details, some simple worked examples, and a bibliography for each section.

This paper presents eMediator, an electronic commerce server prototype that demonstrates ways in which algorithmic support and game-theoretic incentive engineering can jointly improve the efficiency of ecommerce. eAuctionHouse, the configurable auction server, includes a variety of generalized combinatorial auctions and exchanges, pricing schemes, bidding languages, mobile agents, and user support for choosing an auction type. We introduce two new logical bidding languages for combinatorial markets: the XOR bidding language and the OR-of-XORs bidding language. Unlike the traditional OR bidding language, these are fully expressive. They therefore enable the use of the Clarke-Groves pricing mechanism for motivating the bidders to bid truthfully. eAuctionHouse also supports supply/demand curve bidding. eCommitter, the leveled commitment contract optimizer, determines the optimal contract price and decommitting penalties for a variety of leveled commitment contracting mechanisms, taking into account that rational agents will decommit strategically in Nash equilibrium. It also determines the optimal decommitting strategies for any given leveled commitment contract. eExchangeHouse, the safe exchange planner, enables unenforced anonymous exchanges by dividing the exchange into chunks and sequencing those chunks to be delivered safely in alternation between the buyer and the seller.

This work presents a novel approach to efficient multirobot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to being able to withstand communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are given. 1

When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job.

Global communication networks and advances in information technology enable the design of information systems facilitating effective formulation and efficient resolution of negotiation problems. Increasingly, these systems guide negotiators in clarifying the relevant issues, provide media for offer formulation and exchange, and help in achieving an agreement. In practice, the task of analysing, modelling, designing and implementing electronic negotiation media demands a systematic, traceable and reproducible approach. An engineering approach to media specification and construction has these characteristics. In this paper, we provide a rationale for the engineering approach that allows pragmatic adoption of economic and social sciences perspectives on negotiated decisions for the purpose of supporting and undertaking electronic negotiations. Similarities and differences of different theories that underlie on-going studies of electronic negotiations are identified. This provides a basis for integration of different theories and approaches for the specific purpose of the design of effective electronic negotiations. Drawing on diverse streams of literature in different fields such as economics, management, computer, and behavioural sciences, we present an example of an integration of three significant streams of theoretical and applied research involving negotiations, traditional auctions and on-line auctions.

This paper addresses several basic problems inspired by the adaptation of economic mechanisms, and auctions in particular, to the Internet.

Auctions are a basic tool for resource allocation in non-cooperative environments. Much work in computer science, and in artificial intelligence in particular, has been concerned with algorithms for winner determination in auctions in order to maximize a designer's revenue.

We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Eachgroup of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers ’ valuations, are random. The model is a variation of the traditional singleleg, multiperiod revenue management problem, in which consumers act strategically and bid for units of a fixed capacity over time. For this setting, we prove that dynamic variants of the first-price and second-price auction mechanisms maximize the seller’s expected revenue. We also show explicitly how to compute and implement these optimal auctions. The optimal auctions are then compared to a traditional revenue management mechanism—in which list prices are used in each period together with capacity controls—and to a simple auction heuristic that consists of allocating units to eachperiod and running a sequence of standard, multiunit auctions withfixed reserve prices in each period. The traditional revenue management mechanism is proven to be optimal in the limiting cases when there is at most one buyer per period, when capacity is not constraining, and asymptotically when the number of buyers and the capacity increases. The optimal auction significantly outperforms both suboptimal mechanisms when there are a moderate number of periods, capacity is constrained, and the total volume of sales is not too large. The benefit also increases when variability in the dispersion in buyers ’ valuations or in the number of buyers per period increases.

We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then after sufficiently long time, the players' bids are in equilibrium in the one-shot auction in which the types are commonly known.

We consider an augmented version of the symmetric private value auction model with independent types. The augmentation, intended to illustrate reality, concerns information bidders have about their opponents. To the standard assumption that every bidder knows his type and the distribution of types is common knowledge we add the assumption that the ranking of bidders' valuations is common knowledge. This set{up induces a particular asymmetric auction model that raises serious technical difficulties. We prove existence and uniqueness of equilibrium in pure strategies in the two bidder case. We also show that the model generally has no analytic solution. If the distribution of valuations is uniform, both bidders bid pointwise more aggressively relative to the standard symmetric case. However, this property does not apply to all distributions of valuations. Finally, we also provide a numerical solution of equilibrium bid functions for the uniform distribution case.