An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

05011 Abstracts Collection -- Computing and Markets

From 03.01.05 to 07.01.05, the Dagstuhl Seminar 05011``Computing and Markets\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

PARTICIPATION AND LEARNING IN AUCTIONS: BIDDING DECISIONS IN EGYPTIAN OILSEED AUCTIONS

Auctions are common mechanisms for identifying prices and suppliers of commodities and are particularly important in agricultural marketing. Information asymmetries among bidders may be ameliorated over time through some form of learning. In this study, we incorporate prior decisions to participate, information from previous auctions, and firm-specific attributes to explain both the decision to bid and the level of the bid. Our analysis uses data from Egyptian oilseed tenders, an important market both for oilseeds and tendering. Because of the unbalanced nature of the panel data, we are able to evaluate the effects of signals received from previous tenders. We find that firms learn from previous auctions and can gain an informational advantage through some form of representation (e.g., by having an agent and/or direct sales agent to the country). Our results provide strong evidence that learning-by-doing affects the decision to participate and that learning affects the bid value. We also find that firms use outcomes of previous auctions to update information in both their decisions to participate in a market as well as determining the bid level. Finally, we find that firms with representation have a higher probability of participating in auctions and some evidence that they submit higher bids (earning higher returns).auction, bidding, tenders, optimal bids, learning, Marketing,

Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The mechanism provably has Nash equilibria with optimal resource allocation in the static case. For a dynamic environment, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, and a reward signal that reduces information need to a great extent. Empirical results confirm that through learning, agents significantly improve both system and individual performance, e.g., 40% offloading failure rate reduction, 32% communication overhead reduction, up to 38% computation resource savings in low contention, 18% utilization increase with reduced load variation in high contention, and improvement in fairness. Results also confirm the algorithm's good convergence and generalization property in significantly different environments

Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions

Multiattribute auctions, which allow bids on multiple dimensions of the product, are IT-enabled sourcing mechanisms that increase the efficiency of procurement for configurable goods and services compared to price-only auctions. Given the strategic nature of procurement auctions, the amount of information concerning the buyer’s preferences that is disclosed to the suppliers has implications on the profits of the buyer and suppliers and, consequently, on the long-term relationship between them. This study develops novel feedback schemes for multiattribute auctions that protect buyer’s preference information from the supplier and suppliers’ cost schedule from the buyer. We conduct a laboratory experiment to study bidder behavior and profit implications under three different feedback regimes. Our results indicate that bidders are able to extract more profit with more information regarding the state of the auction in terms of provisional allocation and prices. Furthermore, bidding behavior is substantially influenced by the nature and type of feedback

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing