Chapter 1 Approximation and Mechanism Design

  • Published 2011

Abstract

Our world is an interconnected collection of economic and computational systems wherein individuals optimize to achieve their own, perhaps selfish, goals subject to basic laws of the system. Some of these systems perform well, e.g., the national residency matching program which assigns medical students to residency programs in hospitals, e.g., auctions for online advertising on Internet search engines; and some of these systems perform poorly, e.g., financial markets during the 2008 meltdown, e.g., gridlocked transportation networks. The success and failure of these systems depends on the basic laws governing the system. Financial regulation can prevent disastrous market meltdowns, congestion protocols can prevent gridlock in transportation networks, and market and auction design can lead to mechanisms for exchanging goods or services that are good in terms of revenue or social benefit. The two sources for economic considerations are the preferences for individuals and the performance of the system. For instance, bidders in an auction would like to maximize their gains from buying; whereas, the performance of the system could (i.e., from the perspective of the seller) be measured in terms of the revenue it generates. Likewise, the two sources for computational considerations are the individuals who must optimize their strategies, and the system which must enforce its governing rules. For instance, bidders in the auction must figure out how to bid, and the auctioneer must calculate the winner and payments from the bids received. While these calculations may seem easy when auctioning a painting, they both become quite challenging when, e.g., the Federal Communications Commission (FCC) auctions cell phone spectrum for which individual lots have a high-degree of complementarities. These economic and computational systems are complex. The space of individual strategies is complex and the space of possible rules for the system is complex. Optimizing among strategies or system rules in complex environments should lead to complex strategies and system rules, yet the individuals’ strategies or system rules that are successful in practice are often remarkably simple. This simplicity may be a result of computational tractability or due to desired robustness, especially when these desiderata do not significantly sacrifice performance.

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Cite this paper

@inproceedings{2011Chapter1A, title={Chapter 1 Approximation and Mechanism Design}, author={}, year={2011} }