Multiattribute Call Markets.

Multiattribute auctions support automated negotiation in settings where buyers and sellers have valuations for alternate configurations of a good, as defined by configuration attributes. Bidders express offers to buy or sell alternate configurations by specifying configuration-dependent reserve prices, and the auction determines both the traded goods and transaction prices based on these offers. While multiattribute auctions have been deployed in single-buyer procurement settings, the development of double-sided multiattribute auctions-allowing the free participation of both buyers and sellers-has received little attention from academia or industry. In this work I develop a multiattribute call market, a specific type of double auction in which bids accumulate over an extended period of time, before the auction determines trades based on the aggregate collection of bids. Building on a polynomial-time clearing algorithm, I contribute an efficient algorithm for information feedback. Supporting the implementation of market-based algorithms, information feedback support extends the range of settings for which multiattribute call markets achieve efficiency. Multiattribute auctions are only one of many auction variants introduced in recent years. The rapidly growing space of alternative auctions and trading scenarios calls for both a standardized language with which to specify auctions, as well as a computational test environment in which to evaluate alternate designs. I present a novel auction description language and deployment environment that supports the specification of a broad class of auctions, improving on prior approaches through a scripting language that employs both static parameter settings and rule-based behavior invocation. The market game platform, AB3D, can execute these auction scripts to implement multi-auction and multi-agent trading scenarios. The efficiency of multiattribute call markets depends crucially on the underlying valuations of participants. I analyze the expected performance limitations of multiattribute call markets, using existing analytical results where applicable. Addressing a lack of theoretical guidance in many natural settings, I introduce a computational metric on bidder valuations, and show a correlation between this metric and the expected efficiency of multiattribute call markets. As further validation, I integrate multiattribute markets into an existing supply chain simulation, demonstrating efficiency gains over a more conventional negotiation procedure.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60822/1/klochner_1.pd

Rethinking Government Supplier Decisions: The Economic Evaluation of Alternatives (EEoA)

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.1080/10242694.2020.1808939This paper offers an economic model to assist public procurement officials to rank competing vendors when benefits cannot be monetized. An important defense application is ‘source selection’ – choosing the most cost-effective vendor to supply military equipment, facilities, services or supplies. The problem of ranking public investment alternatives when benefits cannot be monetized has spawned an extensive literature that underpins widely applied decision tools. The bulk of the literature, and most government-mandated decision tools, focus on the demand side of a public procurement. The ‘Economic Evaluation of Alternatives’ (EEoA) extends the analysis to the supply side. A unique feature of EEoA is to model vendor decisions in response to government funding projections. Given a parsimonious set of continuously differentiable evaluation criteria, EEoA provides a new tool to rank vendors. In other cases, it offers a valuable consistency check to guide government supplier decisions.U.S. Government affiliation is unstated in article text

Analysis of multi-attribute multi-unit procurement auctions and capacity-constrained sequential auctions

This dissertation examines an iterative multi-attribute auction for multi-unit procurement in the first part. A multi-unit allocation problem that allows order split among suppliers is formulated to improve the market efficiency. Suppliers are allowed to provide discriminative prices over units based on their marginal costs. A mechanism called Iterative Multiple-attribute Multiple-unit Reverse Auction (IMMRA) is proposed based on the assumption of the modified myopic best-response strategies. Numerical experiment results show that the IMMRA achieves market efficiency in most instances. The inefficiency occurs occasionally on the special cases when cost structures are significantly different among suppliers. Numerical results also show that the IMMRA results in lower buyer payments than the Vickrey-Clarke-Grove (VCG) payments in most cases. In the second part, two sequential auctions with the Vickrey-Clarke-Grove (VCG) mechanism are proposed for two buyers to purchase multiple units of an identical item. The invited suppliers are assumed to have capacity constraints of providing the required demands. Three research problems are raised for the analysis of the sequential auctions: the suppliers\u27 expected payoff functions, the suppliers\u27 bidding strategies in the first auction, and the buyers\u27 procurement costs. Because of the intrinsic complexity of the problems, we limit our study to a duopoly market environment with two suppliers. Both suppliers’ dominant bidding strategies are theoretically derived. With numerical experiments, suppliers’ expected profits and buyers’ expected procurement costs are empirically analyzed

Analysis of multi-attribute multi-unit procurement auctions and capacity-constrained sequential auctions

This dissertation examines an iterative multi-attribute auction for multi-unit procurement in the first part. A multi-unit allocation problem that allows order split among suppliers is formulated to improve the market efficiency. Suppliers are allowed to provide discriminative prices over units based on their marginal costs. A mechanism called Iterative Multiple-attribute Multiple-unit Reverse Auction (IMMRA) is proposed based on the assumption of the modified myopic best-response strategies. Numerical experiment results show that the IMMRA achieves market efficiency in most instances. The inefficiency occurs occasionally on the special cases when cost structures are significantly different among suppliers. Numerical results also show that the IMMRA results in lower buyer payments than the Vickrey-Clarke-Grove (VCG) payments in most cases. In the second part, two sequential auctions with the Vickrey-Clarke-Grove (VCG) mechanism are proposed for two buyers to purchase multiple units of an identical item. The invited suppliers are assumed to have capacity constraints of providing the required demands. Three research problems are raised for the analysis of the sequential auctions: the suppliers\u27 expected payoff functions, the suppliers\u27 bidding strategies in the first auction, and the buyers\u27 procurement costs. Because of the intrinsic complexity of the problems, we limit our study to a duopoly market environment with two suppliers. Both suppliers’ dominant bidding strategies are theoretically derived. With numerical experiments, suppliers’ expected profits and buyers’ expected procurement costs are empirically analyzed

Multiple criteria decision making in application layer networks

This work is concerned with the conduct of MCDM by intelligent agents trading commodities in ALNs. These agents consider trustworthiness in their course of negotiation and select offers with respect to product price and seller reputation. --Grid Computing

Estimating the number of new and repeated bidders in construction auctions

The number of new bidders – bidders from whom there is no previous registered participation – is an important variable in most bid tender forecasting models, since the unknown competitive profile of the former strongly limits the predictive accuracy of the latter. Analogously, when a bidder considers entering a bid or when an auctioneer is handling a procurement auction, assessing the likely proportion of experienced bidders is considered an important aspect, as some strategic decisions or even the awarding criteria might differ. However, estimating the number of bidders in a future auction that have not submitted a single bid yet is difficult, since there is no data at all linking their potential participation, an essential requirement for the implementation of any forecasting or estimation method. A practical approach is derived for determining the expected proportion of new bidders to frequent bidders as a function of the population of potential bidders. A multinomial model useful for selective and open tendering is proposed and its performance is validated with a dataset of actual construction auctions. Final remarks concern the valuable information provided by the model to an enduring unsolved bidding problem and the prospects for new research continuations

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

Decentralized Resource Scheduling in Grid/Cloud Computing

In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution