Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment

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 bestresponse. Second, after the auction terminates adjust the prices towards those given in the Vickrey auction, a sealedbid auction in which truth-revelation is optimal. We present an application of this methodology to iBundle, an iterative combinatorial auction which gives optimal allocations for myopic best-response agents.Engineering and Applied Science

Iterative Combinatorial Auctions: Theory and Practice

Combinatorial auctions, which allow agents to bid directly for bundles of resources, are necessary for optimal auction-based solutions to resource allocation problems with agents that have non-additive values for resources, such as distributed scheduling and task assignment problems. We introduce iBundle, the first iterative combinatorial auction that is optimal for a reasonable agent bidding strategy, in this case myopic best-response bidding. Its optimality is proved with a novel connection to primal-dual optimization theory. We demonstrate orders of magnitude performance improvements over the only other known optimal combinatorial auction, the Generalized Vickrey Auction.Engineering and Applied Science

An Ascending-Price Generalized Vickrey Auction

A simple characterization of the equilibrium conditions required to compute Vickrey payments in the Combinatorial Allocation Problem leads to an ascending price Generalized Vickrey Auction. The ascending auc- tion, iBundle Extend & Adjust (iBEA), maintains non-linear and perhaps non-anonymous prices on bundles of items, and terminates with the ef- cient allocation and the Vickrey payments in ex post Nash equilibrium. Crucially, iBEA is able to implement the Vickrey outcome even when the Vickrey payments are not supported in a single competitive equilibrium. The auction closes with Universal competitive equilibrium prices, which provide enough information to compute individualized discounts to adjust the nal prices and implement Vickrey payments.Engineering and Applied Science

Growth and Survival of the Halophyte Salicornia Europaera L. Under Saline Field Conditions

Author Institution: Department of Botany, Ohio UniversityField investigations were carried out to determine growth and survival rates of Salicornia europaea L. in a saline environment at Rittman, Ohio. Collected data indicated that from 62% to 100% of the seedlings within the 'A saline zones investigated did not survive to maturity. Seedling mortality was statistically correlated at P<0.01 to rising soil salinity stress during late spring and summer. Plant growth was minimal between April and June, increasing sharply during late summer and fall

Broadband tunability of gain-flattened quantum well semiconductor lasers with an external grating

Quantum well lasers are shown to exhibit flattened broadband gain spectra at a particular pumping condition. The gain requirement for a grating-tuned external cavity configuration is examined and applied to a semiconductor quantum well laser with an optimized length of gain region. The predicted very broadband tunability of quantum well lasers is confirmed experimentally by grating-tuning of uncoated lasers over 85 nm, with single longitudinal mode output power exceeding 200 mW

An Auction-Based Method for Decentralized Train Scheduling

We present a computational study of an auction-based method for decentralized train scheduling. The method is well suited to the natural information and control structure of mod- ern railroads. We assume separate network territories, with an autonomous dispatch agent responsible for the ow of trains over each territory. Each train is represented by a self-interested agent that bids for the right to travel across the network from its source to destination, submitting bids to multiple dispatch agents along its route as necessary. The bidding language allows trains to bid for the right to enter and exit territories at particular times, and also to represent indifference over a range of times. Computational results on a simple network with straight-forward best-response bid- ding strategies demonstrate that the auction computes near- optimal system-wide schedules. In addition, the method appears to have useful scaling properties, both with the number of trains and with the number of dispatchers, and generates less extremal solutions than those obtained using traditional centralized optimization techniques.Engineering and Applied Science

Learning and Adaption in Multiagent Systems

The goal of a self-interested agent within a multiagent system is to maximize its utility over time. In a situation of strategic interdependence, where the actions of one agent may a ect the utilities of other agents, the optimal behavior of an agent must be conditioned on the expected behaviors of the other agents in the system. Standard game theory assumes that the rationality and preferences of all the agents is common knowledge: each agent is then able to compute the set of possible equilibria, and if there is a unique equilibrium, choose a best-response to the actions that the other agents will all play. Real agents acting within a multiagent system face multiple problems: the agents may have incomplete information about the preferences and rationality of the other agents in the game, computing the equilibria can be computationally complex, and there might be many equilibria from which to choose. An alternative explanation of the emergence of a stable equilibrium is that it arises as the long-run outcome of a repeated game, in which bounded-rational agents adapt their strategies as they learn about the other agents in the system. We review some possible models of learning for games, and then show the pros and cons of using learning in a particular game, the Compensation Mechanism, a mechanism for the efficient coordination of actions within a multiagent system.Engineering and Applied Science

Accounting for Cognitive Costs in On-Line Auction Design

Many auction mechanisms, including first and second price ascending and sealed bid auctions, have been proposed and analyzed in the economics literature. We compare the usefulness of different mechanisms for on-line auctions, focusing on the cognitive costs placed on users (e.g. the cost of determining the value of a good), the possibilities for agent mediation, and the trust properties of the auction. Different auction formats prove to be attractive for agent mediated on-line auctions than for traditional off-line auctions. For example, second price sealed bid auctions are attractive in traditional auctions because they avoid the communication cost of multiple bids in first price ascending auctions, and the “gaming” required to estimate the second highest bid in first price sealed bid auctions. However, when bidding agents are cheap, communication costs cease to be important, and a progressive auction mechanism is preferred over a closed bid auction mechanism, since users with semi-autonomous agents can avoid the cognitive cost of placing an accurate value on a good. As another example, when an on-line auction is being conducted by an untrusted auctioneer (e.g. the auctioneer is selling its own items), rational participants will build bidding agents that transform second price auctions into first price auctions.Engineering and Applied Science