Mochen Yang

University of Minnesota - Twin Cities - Carlson School of Management

19th Avenue South

Minneapolis, MN 55455

United States

SCHOLARLY PAPERS

7

DOWNLOADS
Rank 46,454

SSRN RANKINGS

Top 46,454

in Total Papers Downloads

925

SSRN CITATIONS

3

CROSSREF CITATIONS

4

Scholarly Papers (7)

1.

Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining

Information Systems Research, Forthcoming
Number of pages: 66 Posted: 28 Apr 2017 Last Revised: 01 May 2017
University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities - Carlson School of Management and Carlson School of Management
Downloads 353 (87,599)
Citation 1

Abstract:

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data mining, econometrics, measurement error, misclassification, statistical inference

2.

Understanding User-Generated Content and Customer Engagement on Facebook Business Pages

Information Systems Research, Forthcoming, Kelley School of Business Research Paper No. 18-82
Number of pages: 59 Posted: 27 Oct 2018
University of Minnesota - Twin Cities - Carlson School of Management, Carlson School of Management and University of Minnesota - Twin Cities - Carlson School of Management
Downloads 266 (119,254)
Citation 2

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social media, user-generated content, word-of-mouth, customer engagement, Facebook

3.

Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions

MIS Quarterly, Forthcoming, Kelley School of Business Research Paper No. 19-3
Number of pages: 70 Posted: 19 Dec 2018
University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities - Carlson School of Management and University of Minnesota - Twin Cities - Carlson School of Management
Downloads 91 (292,431)

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continuous combinatorial auctions, multi-unit auctions, electronic markets, real-time bidder support, allocative fairness, computational artifact design

4.

Computational Strategies for Inventory Liquidation

Number of pages: 15 Posted: 13 Dec 2016
University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities, Carlson School of Management, Students and University of Minnesota - Twin Cities - Carlson School of Management
Downloads 85 (305,252)

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5.

Integrating Behavioral, Economic, and Technical Insights to Address Algorithmic Bias: Challenges and Opportunities for IS Research

Number of pages: 33 Posted: 10 Sep 2019
Gediminas Adomavicius and Mochen Yang
University of Minnesota - Twin Cities - Carlson School of Management and University of Minnesota - Twin Cities - Carlson School of Management
Downloads 76 (326,275)

Abstract:

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artificial intelligence, machine learning, augmented decision making, algorithmic bias, human-centric computing, research agenda

6.

Efficient Computational Strategies for Dynamic Inventory Liquidation

Information Systems Research, Forthcoming, Kelley School of Business Research Paper No. 18-79
Number of pages: 74 Posted: 02 Oct 2018
University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Twin Cities - Carlson School of Management and University of Minnesota - Twin Cities - Carlson School of Management
Downloads 54 (389,248)

Abstract:

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inventory liquidation, scalable computational approaches, dynamic programming, greedy heuristic, simulation, design science

7.

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Kelley School of Business Research Paper No. 19-20
Posted: 14 Mar 2019 Last Revised: 03 Jun 2019
University of Minnesota - Twin Cities - Carlson School of Management, University of Minnesota - Minneapolis, University of Minnesota - Twin Cities - Carlson School of Management and University of Minnesota - Twin Cities - Carlson School of Management

Abstract:

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machine learning, econometric analysis, instrumental variable, random forest, causal inference