Revenue considerations in market design

This thesis is about the design and analysis of smart markets for selling commodities and resources. Examples include combinatorial auctions, markets for selling resources, and markets for selling cloud services. The objective here is to maximize the revenue for the market designer. This problem is difficult because the information required for a smart market to function well is usually dispersed and privately held by the participating agents who act strategically. The first part of the thesis is on Bayesian revenue optimal combinatorial auctions. We first quantify a trade-off between the two most commonly used objectives in auction theory: revenue maximization and social welfare maximization. Next, we identify a revenue optimal auction for a benchmark class of package bidding problems, in which each buyer is interested only in a specific bundle and has a value for it, both of which are his private information. Finally, we apply the theory of revenue optimal auctions to analyze two simple pricing schemes - fixed price and an auction based price - for selling cloud computing instances. The second part of the thesis is on designing a market for selling a resource such as a spectrum license or mineral rights. The winning buyer in turn develops this resource to generate profit. We propose a two-stage payment rule where the winning buyer makes an initial payment according to the rules of an auction and also pays a part of the realized profit from the resource according to a prespecified profit-sharing contract (PSC). For the second price auction and the English auction, we show that the two-stage payment offers greater expected revenue to the seller than one-time payment. Further, we show that suitable PSCs provide higher expected total revenue than a one-time payment even when the incentives of the winning buyer to develop the resource must be addressed by the seller. Finally, we consider auctions where bids are in the form of securities whose values to the seller are tied to the eventual realized value of the resource. We obtain ranking of different families of securities in terms of the expected revenue for the seller.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Abhishek, Vineet

Contributors dc:contributor

• Hajek, Bruce
• Williams, Steven R.
• Srikant, Rayadurgam
• Basar, Tamer
• Meyn, Sean P.

• revenue and welfare
• pricing cloud services
• Smart market design
• combinatorial auctions
• profit sharing contracts
• bidding with securities

Statement dc:rights

• Copyright 2012 Vineet Abhishek

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42205

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42205