Decision Support in Complex Market Mechanisms
Making decisions in complex market mechanisms often requires one to process large (e.g., exponential) amounts of information efficiently (e.g., in real time). Such decision task is typically well beyond the cognitive capacity of individuals. The goal of this stream of research is to design theory-based computational tools to support economic transactions and decision making in information-intensive markets.
- Efficient Computational Strategies for Dynamic Inventory Liquidation. Mochen Yang, Gediminas Adomavicius, Alok Gupta. Information Systems Research, 30(2), 595-615, 2019. [Journal Link] [SSRN]
- Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. Gediminas Adomavicius, Alok Gupta, Mochen Yang. MIS Quarterly, 43(3), 721-743, 2019. [Journal Link] [SSRN]
- Providing Real-Time Bidder Support in Multi-Item Multi-Unit Combinatorial Auctions. Gediminas Adomavicius, Alok Gupta, Mochen Yang. working paper.
Robust Statistical Inferences with ML-Generated Covariates
Supervised machine learning enables scalable and cost-effective extraction of useful information from (often unstructured) data, which can be subsequently used in econometric estimations. However, the estimations may suffer from bias due to imperfect machine learning predictions. The goal of this stream of research is to understand the nature of such bias, and design novel approaches to mitigate it.
- Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Mochen Yang, Gediminas Adomavicius, Gordon Burtch, Yuqing Ren. Information Systems Research, 29(1), 4-24, 2018. [Journal Link] [SSRN]
- Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. Mochen Yang, Edward McFowland III, Gordon Burtch, Gediminas Adomavicius. under review. [SSRN]
ML-Augmented Decision Making
Machine learning models (and algorithms in general) play an increasingly important role in many high-stake decision making tasks. Because of the ability to discover meaningful patterns from large amounts of information, these models can augment the capacities of human decision makers. Meanwhile, several issues also arise in ML-augmented decision making, such as algorithmic bias and lack of transparency. This stream of research aims at understanding the dynamics of ML-augmented decision making and designing useful mechanisms to improve its quality.
- Integrating Behavioral, Economic, and Technical Insights to Address Algorithmic Bias: Challenges and Opportunities for IS Research. Gediminas Adomavicius, Mochen Yang. under review. [SSRN]
Dissertation and Related Research: UGC and Engagement on Social Media Brand Pages
My dissertation research investigates the interplay and relationships between digital content and engagement behaviors in business pages on social media, and the related challenges and opportunities for associated firms. Social media platforms such as Facebook empower individual users to interact with each other on firm-hosted business pages. Users can engage with the content created by the firms as well as by other users in multiple ways (liking, commenting, etc.). Such engagement behavior bears important consequences to business, yet its fundamental characteristics are not well-understood. My dissertation research investigates user engagement behaviors toward user-generated content on Facebook business pages. The dissertation can be accessed here.
- Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. Information Systems Research, 30(2), 839–855, 2019. [Journal Link] [SSRN]
- Engagement by Design: An Empirical Study of the “Reactions” Feature on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. under review.
- Social Media to Engage the Global Market: Understanding Cultural Differences in User-Generated Posts on Facebook Business Pages. Yuqing Ren, Maria Rodas, Carlos Torelli, Mochen Yang. under review.