Generating Realistic Data Sets for Combinatorial Auctions

We consider the generation of realistic data sets for combinatorial auctions. This problem has been recognized as central to enhance the contribution of the computer science community to the field. We put forward the notions of structure and budget as main guidelines towards the generation of succinct and realistic input data. We describe a computational framework for the analysis of existing algorithms against realistic benchmarks, and use it in the context of two real world scenarios, i.e., real estate and railroad track auctions. The results of this analysis suggest that the obstacles to using (one round) combinatorial auctions in real world applications might be of an economic nature rather than a computational one

A Data Set Generation Algorithm in Combinatorial Auctions

The generation of realistic data sets in a Combinatorial Auction may be a challenging problem. Well-formed data sets are very useful in the evaluation of algorithms trying to solve the winner determination problem. In this paper a general data set generation scheme is presented, both from an algorithmic and economic point of view. As a case study, a possible auction setting is discussed where the goods on sale are connections between points in space.bid, combinatorial auction, data set generation.

Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve

Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.Comment: Accepted at ICML 201

A multi-agent platform for auction-based allocation of loads in transportation logistics

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings. The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies

Auctions as a vehicle to reduce airport delays and achieve value capture

Congestion at airports imposes large costs on airlines and their passengers. A key reason for congestion is that an airline schedules its flights without regard to the costs imposed on other airlines and their passengers. As a result, during some time intervals, airlines schedule more flights to and from an airport than that airport can accommodate and flights are delayed. This paper explores how a specific market-based proposal by the Federal Aviation Administration (FAA), which includes the use of auctions to determine the right to arrive or depart in a specific time interval at airports in the New York City area, might be used as part of a strategy to mitigate delays and congestion. By explaining the underlying economic theory and key arguments with minimal technical jargon, the paper allows those with little formal training in economics to understand the fundamental issues associated with the FAA's controversial proposal. Moreover, the basics of the proposed auction process, known as a combinatorial auction, and value capture are also explained.Airlines ; Airports

Combinatorial Auction-based Mechanisms for Composite Web Service Selection

Composite service selection presents the opportunity for the rapid development of complex applications using existing web services. It refers to the problem of selecting a set of web services from a large pool of available candidates to logically compose them to achieve value-added composite services. The aim of service selection is to choose the best set of services based on the functional and non-functional (quality related) requirements of a composite service requester. The current service selection approaches mostly assume that web services are offered as single independent entities; there is no possibility for bundling. Moreover, the current research has mainly focused on solving the problem for a single composite service. There is a limited research to date on how the presence of multiple requests for composite services affects the performance of service selection approaches. Addressing these two aspects can significantly enhance the application of composite service selection approaches in the real-world. We develop new approaches for the composite web service selection problem by addressing both the bundling and multiple requests issues. In particular, we propose two mechanisms based on combinatorial auction models, where the provisioning of multiple services are auctioned simultaneously and service providers can bid to offer combinations of web services. We mapped these mechanisms to Integer Linear Programing models and conducted extensive simulations to evaluate them. The results of our experimentation show that bundling can lead to cost reductions compared to when services are offered independently. Moreover, the simultaneous consideration of a set of requests enhances the success rate of the mechanism in allocating services to requests. By considering all composite service requests at the same time, the mechanism achieves more homogenous prices which can be a determining factor for the service requester in choosing the best composite service selection mechanism to deploy

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Market-Based Alternatives for Managing Congestion at New York’s LaGuardia Airport

We summarize the results of a project that was motivated by the expiration of the “High Density Rule,” which defined the slot controls employed at New York’s LaGuardia Airport for more than 30 years. The scope of the project included the analysis of several administrative measures, congestion pricing options and slot auctions. The research output includes a congestion pricing procedure and also the specification of a slot auction mechanism. The research results are based in part on two strategic simulations. These were multi-day events that included the participation of airport operators, most notably the Port Authority of New York and New Jersey, FAA and DOT executives, airline representatives and other members of the air transportation community. The first simulation placed participants in a stressful, high congestion future scenario and then allowed participants to react and problem solve under various administrative measures and congestion pricing options. The second simulation was a mock slot auction in which participants bid on LGA arrival and departure slots for fictitious airlines.Auctions, airport slot auctions, combinatorial auctions