Decision-Theoretic Prediction and Policy Design of GDP Slot Auctions

41 Pages Posted: 19 Apr 2011 Last revised: 3 Apr 2013

See all articles by James Bono

James Bono

Microsoft Corporation

David Wolpert

Santa Fe Institute

Date Written: April 2, 2013

Abstract

We examine the potential for a simple auction to allocate arrival slots during Ground Delay Programs (GDP’s) more efficiently than the currently used sys- tem. The analysis of these auctions uses Predictive Game Theory (PGT) Wolpert and Bono (2010a,b), a new approach that produces a probability distribution over strategies instead of an equilibrium set. We compare the simple auction with other allocation methods, including combinatorial auctions and theoretical benchmarks using data from a one-hour GDP at Chicago Midway. We find that the simple slot auction overcomes several practical shortcomings of other approaches, while offering economically significant efficiency gains with respect to current practices and the potential to lower airline costs. We also find that the second price version of the simple auction dominates the first price version in nearly every decision-relevant category. This is despite the fact that none of the conventional arguments for second price auctions, such as dominant strategy implementability, even apply to GDP slot auctions. Finally, the results indicate that combinatorial auctions, if made operationally practical, might be more efficient than our auction, even though the combinatorial auction does not implement the social optimum in dominant strategies.

Keywords: ground delay program (GDP), predictive game theory (PGT), policy design, distribution-valued solution concept, arrival slot, combinatorial auction

Suggested Citation

Bono, James and Wolpert, David, Decision-Theoretic Prediction and Policy Design of GDP Slot Auctions (April 2, 2013). Available at SSRN: https://ssrn.com/abstract=1815222 or http://dx.doi.org/10.2139/ssrn.1815222

James Bono (Contact Author)

Microsoft Corporation ( email )

One Microsoft Way
Redmond, WA 98052
United States

David Wolpert

Santa Fe Institute ( email )

1399 Hyde Park Road
Santa Fe, NM 897501
United States

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