Empirical mechanism design: Methods, with application to a supply-chain scenario

Yevgeniy Vorobeychik; Christopher Kiekintveld; Michael P. Wellman

Empirical mechanism design: Methods, with application to a supply-chain scenario

Yevgeniy Vorobeychik, Christopher Kiekintveld, Michael P. Wellman

Department of Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

43 Scopus citations

Abstract

Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.

Original language	English
Title of host publication	Proceedings of the 7th ACM Conference on Electronic Commerce 2006
Pages	306-315
Number of pages	10
State	Published - 2006
Event	7th ACM Conference on Electronic Commerce - Ann Arbor, MI, United States Duration: Jun 11 2006 → Jun 15 2006

Publication series

Name	Proceedings of the ACM Conference on Electronic Commerce
Volume	2006

Conference

Conference	7th ACM Conference on Electronic Commerce
Country/Territory	United States
City	Ann Arbor, MI
Period	06/11/06 → 06/15/06

Keywords

Empirical Mechanism Design
Game Theory

Cite this

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title = "Empirical mechanism design: Methods, with application to a supply-chain scenario",

abstract = "Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.",

keywords = "Empirical Mechanism Design, Game Theory",

author = "Yevgeniy Vorobeychik and Christopher Kiekintveld and Wellman, \{Michael P.\}",

year = "2006",

language = "English",

isbn = "1595932364",

series = "Proceedings of the ACM Conference on Electronic Commerce",

pages = "306--315",

booktitle = "Proceedings of the 7th ACM Conference on Electronic Commerce 2006",

note = "7th ACM Conference on Electronic Commerce ; Conference date: 11-06-2006 Through 15-06-2006",

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Empirical mechanism design: Methods, with application to a supply-chain scenario. / Vorobeychik, Yevgeniy; Kiekintveld, Christopher; Wellman, Michael P.
Proceedings of the 7th ACM Conference on Electronic Commerce 2006. 2006. p. 306-315 (Proceedings of the ACM Conference on Electronic Commerce; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Empirical mechanism design

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AU - Vorobeychik, Yevgeniy

AU - Kiekintveld, Christopher

AU - Wellman, Michael P.

PY - 2006

Y1 - 2006

N2 - Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.

AB - Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavior, but the measures proved insufficient. Our empirical mechanism analysis models the relation between a key design parameter and outcomes, confirming the observed behavior and indicating that no reasonable parameter settings would have been likely to achieve the desired effect. More generally, we show that under certain conditions, the estimator of optimal mechanism parameter setting based on empirical data is consistent.

KW - Empirical Mechanism Design

KW - Game Theory

UR - https://www.scopus.com/pages/publications/33748678509

M3 - Conference contribution

AN - SCOPUS:33748678509

SN - 1595932364

SN - 9781595932365

T3 - Proceedings of the ACM Conference on Electronic Commerce

SP - 306

EP - 315

BT - Proceedings of the 7th ACM Conference on Electronic Commerce 2006

Y2 - 11 June 2006 through 15 June 2006

ER -

Empirical mechanism design: Methods, with application to a supply-chain scenario

Abstract

Publication series

Conference

Keywords

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